python: bump version to 2.3.2 to include *all* of the bugfixes (#2171)

Signed-off-by: Jared Van Bortel <jared@nomic.ai>

.circleci/config.yml

@@ -11,9 +11,10 @@ workflows:
           base-revision: main
           config-path: .circleci/continue_config.yml
           mapping: |
+            .circleci/.* run-all-workflows true
+            gpt4all-backend/.* run-all-workflows true
             gpt4all-bindings/python/.* run-python-workflow true
             gpt4all-bindings/typescript/.* run-ts-workflow true
             gpt4all-bindings/csharp/.* run-csharp-workflow true
-            gpt4all-backend/.* run-chat-workflow true
             gpt4all-chat/.* run-chat-workflow true
             .* run-default-workflow true

.circleci/continue_config.yml

@@ -5,6 +5,9 @@ orbs:
   node: circleci/node@5.1
 
 parameters:
+  run-all-workflows:
+    type: boolean
+    default: false
   run-default-workflow:
     type: boolean
     default: false
@@ -39,18 +42,18 @@ jobs:
             git submodule update --init --recursive
       - restore_cache:  # this is the new step to restore cache
           keys:
-            - macos-qt-cache_v2
+            - macos-qt-cache-v3
       - run:
           name: Installing Qt
           command: |
             if [ ! -d ~/Qt ]; then
               curl -o qt-unified-macOS-x64-4.6.0-online.dmg https://gpt4all.io/ci/qt-unified-macOS-x64-4.6.0-online.dmg
               hdiutil attach qt-unified-macOS-x64-4.6.0-online.dmg
-              /Volumes/qt-unified-macOS-x64-4.6.0-online/qt-unified-macOS-x64-4.6.0-online.app/Contents/MacOS/qt-unified-macOS-x64-4.6.0-online --no-force-installations --no-default-installations --no-size-checking --default-answer --accept-licenses --confirm-command --accept-obligations --email $QT_EMAIL --password $QT_PASSWORD install qt.tools.cmake qt.tools.ifw.46 qt.tools.ninja qt.qt6.651.clang_64 qt.qt6.651.qt5compat qt.qt6.651.debug_info qt.qt6.651.addons.qtpdf qt.qt6.651.addons.qthttpserver
+              /Volumes/qt-unified-macOS-x64-4.6.0-online/qt-unified-macOS-x64-4.6.0-online.app/Contents/MacOS/qt-unified-macOS-x64-4.6.0-online --no-force-installations --no-default-installations --no-size-checking --default-answer --accept-licenses --confirm-command --accept-obligations --email $QT_EMAIL --password $QT_PASSWORD install qt.tools.cmake qt.tools.ifw.47 qt.tools.ninja qt.qt6.651.clang_64 qt.qt6.651.qt5compat qt.qt6.651.debug_info qt.qt6.651.addons.qtpdf qt.qt6.651.addons.qthttpserver
               hdiutil detach /Volumes/qt-unified-macOS-x64-4.6.0-online
             fi
       - save_cache:  # this is the new step to save cache
-          key: macos-qt-cache_v2
+          key: macos-qt-cache-v3
           paths:
             - ~/Qt
       - run:
@@ -58,7 +61,7 @@ jobs:
           command: |
             mkdir build
             cd build
-            export PATH=$PATH:$HOME/Qt/Tools/QtInstallerFramework/4.6/bin
+            export PATH=$PATH:$HOME/Qt/Tools/QtInstallerFramework/4.7/bin
             ~/Qt/Tools/CMake/CMake.app/Contents/bin/cmake \
               -DCMAKE_GENERATOR:STRING=Ninja \
               -DBUILD_UNIVERSAL=ON \
@@ -88,7 +91,7 @@ jobs:
             git submodule update --init --recursive
       - restore_cache:  # this is the new step to restore cache
           keys:
-            - linux-qt-cache
+            - linux-qt-cache-v2
       - run:
           name: Setup Linux and Dependencies
           command: |
@@ -101,10 +104,10 @@ jobs:
             if [ ! -d ~/Qt ]; then
               wget https://gpt4all.io/ci/qt-unified-linux-x64-4.6.0-online.run
               chmod +x qt-unified-linux-x64-4.6.0-online.run
-              ./qt-unified-linux-x64-4.6.0-online.run --no-force-installations --no-default-installations --no-size-checking --default-answer --accept-licenses --confirm-command --accept-obligations --email $QT_EMAIL --password $QT_PASSWORD install qt.tools.cmake qt.tools.ifw.46 qt.tools.ninja qt.qt6.651.gcc_64 qt.qt6.651.qt5compat qt.qt6.651.debug_info qt.qt6.651.addons.qtpdf qt.qt6.651.addons.qthttpserver qt.qt6.651.qtwaylandcompositor
+              ./qt-unified-linux-x64-4.6.0-online.run --no-force-installations --no-default-installations --no-size-checking --default-answer --accept-licenses --confirm-command --accept-obligations --email $QT_EMAIL --password $QT_PASSWORD install qt.tools.cmake qt.tools.ifw.47 qt.tools.ninja qt.qt6.651.gcc_64 qt.qt6.651.qt5compat qt.qt6.651.debug_info qt.qt6.651.addons.qtpdf qt.qt6.651.addons.qthttpserver qt.qt6.651.qtwaylandcompositor
             fi
       - save_cache:  # this is the new step to save cache
-          key: linux-qt-cache
+          key: linux-qt-cache-v2
           paths:
             - ~/Qt
       - run:
@@ -117,7 +120,7 @@ jobs:
           command: |
             set -eo pipefail
             export CMAKE_PREFIX_PATH=~/Qt/6.5.1/gcc_64/lib/cmake
-            export PATH=$PATH:$HOME/Qt/Tools/QtInstallerFramework/4.6/bin
+            export PATH=$PATH:$HOME/Qt/Tools/QtInstallerFramework/4.7/bin
             mkdir build
             cd build
             mkdir upload
@@ -142,16 +145,16 @@ jobs:
             git submodule update --init --recursive
       - restore_cache:  # this is the new step to restore cache
           keys:
-            - windows-qt-cache
+            - windows-qt-cache-v2
       - run:
           name: Installing Qt
           command: |
             if (-not (Test-Path C:\Qt)) {
               Invoke-WebRequest -Uri https://gpt4all.io/ci/qt-unified-windows-x64-4.6.0-online.exe -OutFile qt-unified-windows-x64-4.6.0-online.exe
-              & .\qt-unified-windows-x64-4.6.0-online.exe --no-force-installations --no-default-installations --no-size-checking --default-answer --accept-licenses --confirm-command --accept-obligations --email ${Env:QT_EMAIL} --password ${Env:QT_PASSWORD} install qt.tools.cmake qt.tools.ifw.46 qt.tools.ninja qt.qt6.651.win64_msvc2019_64 qt.qt6.651.qt5compat qt.qt6.651.debug_info qt.qt6.651.addons.qtpdf qt.qt6.651.addons.qthttpserver
+              & .\qt-unified-windows-x64-4.6.0-online.exe --no-force-installations --no-default-installations --no-size-checking --default-answer --accept-licenses --confirm-command --accept-obligations --email ${Env:QT_EMAIL} --password ${Env:QT_PASSWORD} install qt.tools.cmake qt.tools.ifw.47 qt.tools.ninja qt.qt6.651.win64_msvc2019_64 qt.qt6.651.qt5compat qt.qt6.651.debug_info qt.qt6.651.addons.qtpdf qt.qt6.651.addons.qthttpserver
             }
       - save_cache:  # this is the new step to save cache
-          key: windows-qt-cache
+          key: windows-qt-cache-v2
           paths:
             - C:\Qt
       - run:
@@ -166,7 +169,7 @@ jobs:
             $Env:PATH = "${Env:PATH};C:\Program Files (x86)\Windows Kits\10\bin\10.0.22000.0\x64"
             $Env:PATH = "${Env:PATH};C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Tools\MSVC\14.29.30133\bin\HostX64\x64"
             $Env:PATH = "${Env:PATH};C:\VulkanSDK\1.3.261.1\bin"
-            $Env:PATH = "${Env:PATH};C:\Qt\Tools\QtInstallerFramework\4.6\bin"
+            $Env:PATH = "${Env:PATH};C:\Qt\Tools\QtInstallerFramework\4.7\bin"
             $Env:LIB = "${Env:LIB};C:\Program Files (x86)\Windows Kits\10\Lib\10.0.22000.0\ucrt\x64"
             $Env:LIB = "${Env:LIB};C:\Program Files (x86)\Windows Kits\10\Lib\10.0.22000.0\um\x64"
             $Env:LIB = "${Env:LIB};C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Tools\MSVC\14.29.30133\lib\x64"
@@ -209,7 +212,7 @@ jobs:
             git submodule update --init --recursive
       - restore_cache:  # this is the new step to restore cache
           keys:
-            - linux-qt-cache
+            - linux-qt-cache-v2
       - run:
           name: Setup Linux and Dependencies
           command: |
@@ -222,20 +225,18 @@ jobs:
             if [ ! -d ~/Qt ]; then
               wget https://gpt4all.io/ci/qt-unified-linux-x64-4.6.0-online.run
               chmod +x qt-unified-linux-x64-4.6.0-online.run
-              ./qt-unified-linux-x64-4.6.0-online.run --no-force-installations --no-default-installations --no-size-checking --default-answer --accept-licenses --confirm-command --accept-obligations --email $QT_EMAIL --password $QT_PASSWORD install qt.tools.cmake qt.tools.ifw.46 qt.tools.ninja qt.qt6.651.gcc_64 qt.qt6.651.qt5compat qt.qt6.651.debug_info qt.qt6.651.addons.qtpdf qt.qt6.651.addons.qthttpserver qt.qt6.651.qtwaylandcompositor
+              ./qt-unified-linux-x64-4.6.0-online.run --no-force-installations --no-default-installations --no-size-checking --default-answer --accept-licenses --confirm-command --accept-obligations --email $QT_EMAIL --password $QT_PASSWORD install qt.tools.cmake qt.tools.ifw.47 qt.tools.ninja qt.qt6.651.gcc_64 qt.qt6.651.qt5compat qt.qt6.651.debug_info qt.qt6.651.addons.qtpdf qt.qt6.651.addons.qthttpserver qt.qt6.651.qtwaylandcompositor
             fi
       - save_cache:  # this is the new step to save cache
-          key: linux-qt-cache
+          key: linux-qt-cache-v2
           paths:
             - ~/Qt
       - run:
           name: Build
           command: |
             export CMAKE_PREFIX_PATH=~/Qt/6.5.1/gcc_64/lib/cmake
-            mkdir build
-            cd build
-            ~/Qt/Tools/CMake/bin/cmake -DCMAKE_BUILD_TYPE=Release -S ../gpt4all-chat -B .
-            ~/Qt/Tools/CMake/bin/cmake --build . --target all
+            ~/Qt/Tools/CMake/bin/cmake -DCMAKE_BUILD_TYPE=Release -S gpt4all-chat -B build
+            ~/Qt/Tools/CMake/bin/cmake --build build --target all
 
   build-gpt4all-chat-windows:
     machine:
@@ -251,16 +252,16 @@ jobs:
             git submodule update --init --recursive
       - restore_cache:  # this is the new step to restore cache
           keys:
-            - windows-qt-cache
+            - windows-qt-cache-v2
       - run:
           name: Installing Qt
           command: |
             if (-not (Test-Path C:\Qt)) {
               Invoke-WebRequest -Uri https://gpt4all.io/ci/qt-unified-windows-x64-4.6.0-online.exe -OutFile qt-unified-windows-x64-4.6.0-online.exe
-              & .\qt-unified-windows-x64-4.6.0-online.exe --no-force-installations --no-default-installations --no-size-checking --default-answer --accept-licenses --confirm-command --accept-obligations --email ${Env:QT_EMAIL} --password ${Env:QT_PASSWORD} install qt.tools.cmake qt.tools.ifw.46 qt.tools.ninja qt.qt6.651.win64_msvc2019_64 qt.qt6.651.qt5compat qt.qt6.651.debug_info qt.qt6.651.addons.qtpdf qt.qt6.651.addons.qthttpserver
+              & .\qt-unified-windows-x64-4.6.0-online.exe --no-force-installations --no-default-installations --no-size-checking --default-answer --accept-licenses --confirm-command --accept-obligations --email ${Env:QT_EMAIL} --password ${Env:QT_PASSWORD} install qt.tools.cmake qt.tools.ifw.47 qt.tools.ninja qt.qt6.651.win64_msvc2019_64 qt.qt6.651.qt5compat qt.qt6.651.debug_info qt.qt6.651.addons.qtpdf qt.qt6.651.addons.qthttpserver
             }
       - save_cache:  # this is the new step to save cache
-          key: windows-qt-cache
+          key: windows-qt-cache-v2
           paths:
             - C:\Qt
       - run:
@@ -287,17 +288,16 @@ jobs:
             $Env:INCLUDE = "${Env:INCLUDE};C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Auxiliary\VS\include"
             $Env:INCLUDE = "${Env:INCLUDE};C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Tools\MSVC\14.29.30133\include"
             $Env:INCLUDE = "${Env:INCLUDE};C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Tools\MSVC\14.29.30133\ATLMFC\include"
-            mkdir build
-            cd build
+            $Env:VULKAN_SDK = "C:\VulkanSDK\1.3.261.1"
             & "C:\Qt\Tools\CMake_64\bin\cmake.exe" `
               "-DCMAKE_GENERATOR:STRING=Ninja" `
               "-DCMAKE_BUILD_TYPE=Release" `
               "-DCMAKE_PREFIX_PATH:PATH=C:\Qt\6.5.1\msvc2019_64" `
               "-DCMAKE_MAKE_PROGRAM:FILEPATH=C:\Qt\Tools\Ninja\ninja.exe" `
               "-DKOMPUTE_OPT_DISABLE_VULKAN_VERSION_CHECK=ON" `
-              "-S ..\gpt4all-chat" `
-              "-B ."
-            & "C:\Qt\Tools\Ninja\ninja.exe"
+              "-S gpt4all-chat" `
+              "-B build"
+            & "C:\Qt\Tools\Ninja\ninja.exe" -C build
 
   build-gpt4all-chat-macos:
     macos:
@@ -311,52 +311,50 @@ jobs:
             git submodule update --init --recursive
       - restore_cache:  # this is the new step to restore cache
           keys:
-            - macos-qt-cache_v2
+            - macos-qt-cache-v3
       - run:
           name: Installing Qt
           command: |
             if [ ! -d ~/Qt ]; then
               curl -o qt-unified-macOS-x64-4.6.0-online.dmg https://gpt4all.io/ci/qt-unified-macOS-x64-4.6.0-online.dmg
               hdiutil attach qt-unified-macOS-x64-4.6.0-online.dmg
-              /Volumes/qt-unified-macOS-x64-4.6.0-online/qt-unified-macOS-x64-4.6.0-online.app/Contents/MacOS/qt-unified-macOS-x64-4.6.0-online --no-force-installations --no-default-installations --no-size-checking --default-answer --accept-licenses --confirm-command --accept-obligations --email $QT_EMAIL --password $QT_PASSWORD install qt.tools.cmake qt.tools.ifw.46 qt.tools.ninja qt.qt6.651.clang_64 qt.qt6.651.qt5compat qt.qt6.651.debug_info qt.qt6.651.addons.qtpdf qt.qt6.651.addons.qthttpserver
+              /Volumes/qt-unified-macOS-x64-4.6.0-online/qt-unified-macOS-x64-4.6.0-online.app/Contents/MacOS/qt-unified-macOS-x64-4.6.0-online --no-force-installations --no-default-installations --no-size-checking --default-answer --accept-licenses --confirm-command --accept-obligations --email $QT_EMAIL --password $QT_PASSWORD install qt.tools.cmake qt.tools.ifw.47 qt.tools.ninja qt.qt6.651.clang_64 qt.qt6.651.qt5compat qt.qt6.651.debug_info qt.qt6.651.addons.qtpdf qt.qt6.651.addons.qthttpserver
               hdiutil detach /Volumes/qt-unified-macOS-x64-4.6.0-online
             fi
       - save_cache:  # this is the new step to save cache
-          key: macos-qt-cache_v2
+          key: macos-qt-cache-v3
           paths:
             - ~/Qt
       - run:
           name: Build
           command: |
-            mkdir build
-            cd build
             ~/Qt/Tools/CMake/CMake.app/Contents/bin/cmake \
               -DCMAKE_GENERATOR:STRING=Ninja \
               -DBUILD_UNIVERSAL=ON \
               -DCMAKE_BUILD_TYPE=Release \
               -DCMAKE_PREFIX_PATH:PATH=~/Qt/6.5.1/macos/lib/cmake/Qt6 \
               -DCMAKE_MAKE_PROGRAM:FILEPATH=~/Qt/Tools/Ninja/ninja \
-              -S ../gpt4all-chat \
-              -B .
-            ~/Qt/Tools/CMake/CMake.app/Contents/bin/cmake --build . --target all
+              -S gpt4all-chat \
+              -B build
+            ~/Qt/Tools/CMake/CMake.app/Contents/bin/cmake --build build --target all
   build-ts-docs: 
     docker: 
       - image: cimg/base:stable
     steps:
       - checkout
       - node/install:
-          install-yarn: true
           node-version: "18.16"
       - run: node --version
+      - run: corepack enable
       - node/install-packages:
-          pkg-manager: yarn
+          pkg-manager: npm
           app-dir: gpt4all-bindings/typescript
-          override-ci-command: yarn install
+          override-ci-command: npm install --ignore-scripts
       - run:
           name: build docs ts yo
           command: |
             cd gpt4all-bindings/typescript
-            yarn docs:build
+            npm run docs:build
   build-py-docs:
     docker:
       - image: circleci/python:3.8
@@ -402,13 +400,10 @@ jobs:
       - run:
           name: Build C library
           command: |
-            git submodule init
-            git submodule update
+            git submodule update --init --recursive
             cd gpt4all-backend
-            mkdir build
-            cd build
-            cmake ..
-            cmake --build . --parallel
+            cmake -B build
+            cmake --build build --parallel
       - run:
           name: Build wheel
           command: |
@@ -435,13 +430,10 @@ jobs:
       - run:
           name: Build C library
           command: |
-            git submodule init
-            git submodule update
+            git submodule update --init  # don't use --recursive because macOS doesn't use Kompute
             cd gpt4all-backend
-            mkdir build
-            cd build
-            cmake .. -DCMAKE_OSX_ARCHITECTURES="x86_64;arm64"
-            cmake --build . --parallel
+            cmake -B build -DCMAKE_OSX_ARCHITECTURES="x86_64;arm64"
+            cmake --build build --parallel
       - run:
           name: Build wheel
           command: |
@@ -477,15 +469,13 @@ jobs:
       - run:
           name: Build C library
           command: |
-            git submodule init
-            git submodule update
+            git submodule update --init --recursive
             cd gpt4all-backend
-            mkdir build
-            cd build
-            $env:Path += ";C:\ProgramData\mingw64\mingw64\bin"
-            $env:Path += ";C:\VulkanSDK\1.3.261.1\bin"
-            cmake -G "MinGW Makefiles" .. -DKOMPUTE_OPT_DISABLE_VULKAN_VERSION_CHECK=ON -DKOMPUTE_OPT_USE_BUILT_IN_VULKAN_HEADER=OFF
-            cmake --build . --parallel
+            $Env:Path += ";C:\ProgramData\mingw64\mingw64\bin"
+            $Env:Path += ";C:\VulkanSDK\1.3.261.1\bin"
+            $Env:VULKAN_SDK = "C:\VulkanSDK\1.3.261.1"
+            cmake -G "MinGW Makefiles" -B build -DKOMPUTE_OPT_DISABLE_VULKAN_VERSION_CHECK=ON -DKOMPUTE_OPT_USE_BUILT_IN_VULKAN_HEADER=OFF
+            cmake --build build --parallel
       - run:
           name: Build wheel
           # TODO: As part of this task, we need to move mingw64 binaries into package.
@@ -620,6 +610,7 @@ jobs:
             $Env:Path += ";$MinGwBin"
             $Env:Path += ";C:\Program Files\CMake\bin"
             $Env:Path += ";C:\VulkanSDK\1.3.261.1\bin"
+            $Env:VULKAN_SDK = "C:\VulkanSDK\1.3.261.1"
             cd gpt4all-backend
             mkdir runtimes/win-x64
             cd runtimes/win-x64
@@ -660,6 +651,7 @@ jobs:
           command: |
             $Env:Path += ";C:\Program Files\CMake\bin"
             $Env:Path += ";C:\VulkanSDK\1.3.261.1\bin"
+            $Env:VULKAN_SDK = "C:\VulkanSDK\1.3.261.1"
             cd gpt4all-backend
             mkdir runtimes/win-x64_msvc
             cd runtimes/win-x64_msvc
@@ -673,7 +665,7 @@ jobs:
 
   build-csharp-linux:
     docker:
-      - image: mcr.microsoft.com/dotnet/sdk:7.0-jammy # Ubuntu 22.04
+      - image: mcr.microsoft.com/dotnet/sdk:8.0
     steps:
       - checkout
       - attach_workspace:
@@ -729,6 +721,10 @@ jobs:
             - gpt4all-csharp-nuget-packages-win
       - attach_workspace:
           at: C:\Users\circleci\workspace
+      - run:
+          name: "Install .NET"
+          command: |
+            choco install -y dotnet-8.0-sdk
       - run:
           name: "Prepare Native Libs"
           command: |
@@ -776,7 +772,8 @@ jobs:
       - run:
           name: Install dependencies
           command: |
-            brew install --cask dotnet-sdk
+            brew tap isen-ng/dotnet-sdk-versions
+            brew install --cask dotnet-sdk8-0-100
       - attach_workspace:
           at: /tmp/workspace
       - run:
@@ -818,7 +815,7 @@ jobs:
 
   store-and-upload-nupkgs:
     docker:
-      - image: mcr.microsoft.com/dotnet/sdk:6.0-jammy # Ubuntu 22.04
+      - image: mcr.microsoft.com/dotnet/sdk:8.0
     steps:
       - attach_workspace:
           at: /tmp/workspace
@@ -834,9 +831,9 @@ jobs:
             cp /tmp/workspace/runtimes/linux-x64/*.so runtimes/linux-x64/native/
             mkdir -p runtimes/win-x64/native
             cp /tmp/workspace/runtimes/win-x64/*.dll runtimes/win-x64/native/
-            mkdir -p runtimes/osx/native
-            cp /tmp/workspace/runtimes/osx-x64/*.dylib runtimes/osx/native/
-            cp /tmp/workspace/runtimes/osx-x64/*.metal runtimes/osx/native/
+            #mkdir -p runtimes/osx/native
+            #cp /tmp/workspace/runtimes/osx-x64/*.dylib runtimes/osx/native/
+            #cp /tmp/workspace/runtimes/osx-x64/*.metal runtimes/osx/native/
             dotnet pack ./Gpt4All/Gpt4All.csproj -p:IncludeSymbols=true -p:SymbolPackageFormat=snupkg -c Release
             dotnet nuget push ./Gpt4All/bin/Release/Gpt4All.*.nupkg -s $NUGET_URL -k $NUGET_TOKEN --skip-duplicate
       - store_artifacts:
@@ -853,9 +850,11 @@ jobs:
           install-yarn: true
           node-version: "18.16"
       - run: node --version
+      - run: corepack enable
       - node/install-packages:
           app-dir: gpt4all-bindings/typescript
           pkg-manager: yarn
+          override-ci-command: yarn install
       - run:
           command: | 
             cd gpt4all-bindings/typescript
@@ -882,9 +881,11 @@ jobs:
           install-yarn: true
           node-version: "18.16"
       - run: node --version
+      - run: corepack enable
       - node/install-packages:
           app-dir: gpt4all-bindings/typescript
           pkg-manager: yarn
+          override-ci-command: yarn install
       - run:
           command: | 
             cd gpt4all-bindings/typescript
@@ -893,14 +894,14 @@ jobs:
           name: "Persisting all necessary things to workspace"
           command: |  
             mkdir -p gpt4all-backend/prebuilds/darwin-x64
-            mkdir -p gpt4all-backend/runtimes/darwin-x64
-            cp /tmp/gpt4all-backend/runtimes/osx-x64/*-*.* gpt4all-backend/runtimes/darwin-x64
+            mkdir -p gpt4all-backend/runtimes/darwin
+            cp /tmp/gpt4all-backend/runtimes/osx-x64/*-*.* gpt4all-backend/runtimes/darwin
             cp gpt4all-bindings/typescript/prebuilds/darwin-x64/*.node gpt4all-backend/prebuilds/darwin-x64
       - persist_to_workspace:
           root: gpt4all-backend
           paths:
             - prebuilds/darwin-x64/*.node
-            - runtimes/darwin-x64/*-*.*
+            - runtimes/darwin/*-*.*
 
   build-nodejs-windows: 
     executor:
@@ -922,6 +923,7 @@ jobs:
               nvm install 18.16.0
               nvm use 18.16.0
       - run: node --version 
+      - run: corepack enable
       - run:           
           command: |
             npm install -g yarn
@@ -955,6 +957,7 @@ jobs:
           install-yarn: true
           node-version: "18.16"
       - run: node --version
+      - run: corepack enable
       - run: 
           command: |
             cd gpt4all-bindings/typescript
@@ -969,9 +972,12 @@ jobs:
             cp /tmp/gpt4all-backend/runtimes/linux-x64/*-*.so runtimes/linux-x64/native/ 
             cp /tmp/gpt4all-backend/prebuilds/linux-x64/*.node prebuilds/linux-x64/    
 
-            mkdir -p runtimes/darwin-x64/native
+            # darwin has univeral runtime libraries
+            mkdir -p runtimes/darwin/native
             mkdir -p prebuilds/darwin-x64/
-            cp /tmp/gpt4all-backend/runtimes/darwin-x64/*-*.* runtimes/darwin-x64/native/
+
+            cp /tmp/gpt4all-backend/runtimes/darwin/*-*.* runtimes/darwin/native/
+
             cp /tmp/gpt4all-backend/prebuilds/darwin-x64/*.node prebuilds/darwin-x64/    
             
             # Fallback build if user is not on above prebuilds
@@ -994,16 +1000,22 @@ jobs:
           command: |
             cd gpt4all-bindings/typescript
             npm set //registry.npmjs.org/:_authToken=$NPM_TOKEN
-            npm publish --access public --tag alpha
+            npm publish
 
 workflows:
   version: 2
   default:
-    when: << pipeline.parameters.run-default-workflow >>
+    when:
+      or:
+       - << pipeline.parameters.run-all-workflows >>
+       - << pipeline.parameters.run-default-workflow >>
     jobs:
       - default-job
   build-chat-offline-installers:
-    when: << pipeline.parameters.run-chat-workflow >>
+    when:
+      or:
+        - << pipeline.parameters.run-all-workflows >>
+        - << pipeline.parameters.run-chat-workflow >>
     jobs:
       - hold:
           type: approval
@@ -1017,7 +1029,10 @@ workflows:
           requires:
             - hold
   build-and-test-gpt4all-chat:
-    when: << pipeline.parameters.run-chat-workflow >>
+    when:
+      or:
+        - << pipeline.parameters.run-all-workflows >>
+        - << pipeline.parameters.run-chat-workflow >>
     jobs:
       - hold:
           type: approval
@@ -1031,7 +1046,10 @@ workflows:
           requires:
             - hold
   deploy-docs:
-    when: << pipeline.parameters.run-python-workflow >>
+    when:
+      or:
+        - << pipeline.parameters.run-all-workflows >>
+        - << pipeline.parameters.run-python-workflow >>
     jobs:
       - build-ts-docs:
           filters:
@@ -1044,7 +1062,10 @@ workflows:
               only:
                 - main
   build-py-deploy:
-    when: << pipeline.parameters.run-python-workflow >>
+    when:
+      or:
+        - << pipeline.parameters.run-all-workflows >>
+        - << pipeline.parameters.run-python-workflow >>
     jobs:
       - pypi-hold:
           type: approval
@@ -1079,15 +1100,20 @@ workflows:
             - build-py-macos
   build-bindings:
     when: 
-        or:
-         - << pipeline.parameters.run-python-workflow >>
-         - << pipeline.parameters.run-csharp-workflow >>
-         - <<   pipeline.parameters.run-ts-workflow   >>
+      or:
+       - << pipeline.parameters.run-all-workflows >>
+       - << pipeline.parameters.run-python-workflow >>
+       - << pipeline.parameters.run-csharp-workflow >>
+       - << pipeline.parameters.run-ts-workflow >>
     jobs:
       - hold:
           type: approval
+      - csharp-hold:
+          type: approval
       - nuget-hold:
           type: approval
+      - nodejs-hold:
+          type: approval
       - npm-hold:
           type: approval
       - build-bindings-backend-linux:
@@ -1130,21 +1156,21 @@ workflows:
             branches:
               only:
           requires:
-            - npm-hold
+            - nodejs-hold
             - build-bindings-backend-linux
       - build-nodejs-windows:
           filters:
             branches:
               only:
           requires:
-            - npm-hold
+            - nodejs-hold
             - build-bindings-backend-windows-msvc
       - build-nodejs-macos:
           filters:
             branches:
               only:
           requires:
-            - npm-hold
+            - nodejs-hold
             - build-bindings-backend-macos
 
 
@@ -1154,21 +1180,21 @@ workflows:
             branches:
               only:
           requires:
-            - nuget-hold
+            - csharp-hold
             - build-bindings-backend-linux
       - build-csharp-windows:
           filters:
             branches:
               only:
           requires:
-            - nuget-hold
+            - csharp-hold
             - build-bindings-backend-windows
       - build-csharp-macos:
           filters:
             branches:
               only:
           requires:
-            - nuget-hold
+            - csharp-hold
             - build-bindings-backend-macos
       - store-and-upload-nupkgs:
           filters:
@@ -1178,4 +1204,4 @@ workflows:
             - nuget-hold
             - build-csharp-windows
             - build-csharp-linux
-            - build-csharp-macos
+            #- build-csharp-macos

.codespellrc

@@ -1,3 +1,3 @@
 [codespell]
-ignore-words-list = blong, belong, afterall, som, assistent
+ignore-words-list = blong, afterall, som, assistent, crasher
 skip = .git,*.pdf,*.svg,*.lock

.github/ISSUE_TEMPLATE/bindings-bug.md

@@ -0,0 +1,35 @@
+---
+name: "\U0001F6E0 Bindings Bug Report"
+about: A bug report for the GPT4All Bindings
+labels: ["bindings", "bug-unconfirmed"]
+---
+
+<!-- Before creating a new issue, please make sure to take a few moments to check the issue tracker for existing issues about the bug. -->
+
+### Bug Report
+
+<!-- A clear and concise description of what the bug is. -->
+
+### Example Code
+
+<!-- Please provide a minimal code example that can be used to experience this issue. Delete this section if it does not apply. -->
+
+### Steps to Reproduce
+
+<!-- List the steps that should be taken to experience this issue. -->
+
+1.
+2.
+3.
+
+### Expected Behavior
+
+<!-- In a few words, what did you expect to happen? -->
+
+### Your Environment
+
+- Bindings version (e.g. "Version" from `pip show gpt4all`):
+- Operating System:
+- Chat model used (if applicable):
+
+<!-- You can freely edit this text, please remove all the lines you believe are unnecessary. -->

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -1,55 +0,0 @@
-name: "\U0001F41B Bug Report"
-description: Submit a bug report to help us improve GPT4All
-labels: ["02 Bug Report"]
-body:
-  - type: markdown
-    attributes:
-      value: >
-        Thank you for taking the time to file a bug report. Before creating a new
-        issue, please make sure to take a few moments to check the issue tracker
-        for existing issues about the bug.
-
-  - type: textarea
-    id: system-info
-    attributes:
-      label: System Info
-      description: Please share your system info with us.
-      placeholder: GPT4All version, platform, python version, etc...
-    validations:
-      required: true
-
-  - type: checkboxes
-    id: information-scripts-examples
-    attributes:
-      label: Information
-      description: "The problem arises when using:"
-      options:
-        - label: "The official example notebooks/scripts"
-        - label: "My own modified scripts"
-
-  - type: textarea
-    id: reproduction
-    validations:
-      required: true
-    attributes:
-      label: Reproduction
-      description: |
-        Please provide a [code sample](https://stackoverflow.com/help/minimal-reproducible-example) that reproduces the problem you ran into. It can be a Colab link or just a code snippet.
-        If you have code snippets, error messages, stack traces please provide them here as well.
-        Important! Use code tags to correctly format your code. See https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks#syntax-highlighting
-        Avoid screenshots when possible, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.
-
-      placeholder: |
-        Steps to reproduce the behavior:
-
-          1.
-          2.
-          3.
-
-  - type: textarea
-    id: expected-behavior
-    validations:
-      required: true
-    attributes:
-      label: Expected behavior
-      description: "A clear and concise description of what you would expect to happen."

.github/ISSUE_TEMPLATE/chat-bug.md

@@ -0,0 +1,31 @@
+---
+name: "\U0001F4AC GPT4All Bug Report"
+about: A bug report for GPT4All Chat
+labels: ["chat", "bug-unconfirmed"]
+---
+
+<!-- Before creating a new issue, please make sure to take a few moments to check the issue tracker for existing issues about the bug. -->
+
+### Bug Report
+
+<!-- A clear and concise description of what the bug is. -->
+
+### Steps to Reproduce
+
+<!-- List the steps that should be taken to experience this issue. Provide any relevant information about your configuration, and describe anything that was unexpected. -->
+
+1.
+2.
+3.
+
+### Expected Behavior
+
+<!-- In a few words, what did you expect to happen? -->
+
+### Your Environment
+
+- GPT4All version:
+- Operating System:
+- Chat model used (if applicable):
+
+<!-- You can freely edit this text, please remove all the lines you believe are unnecessary. -->

.github/ISSUE_TEMPLATE/config.yml

@@ -1,2 +1 @@
-blank_issues_enabled: false
-version: 2.1
+version: 2.1

.github/ISSUE_TEMPLATE/documentation.md

@@ -0,0 +1,9 @@
+---
+name: "\U0001F4C4 Documentation"
+about: An issue related to the GPT4All documentation
+labels: ["documentation"]
+---
+
+### Documentation
+
+<!-- Please describe the issue with the documentation as clearly as possible. -->

.github/ISSUE_TEMPLATE/documentation.yml

@@ -1,19 +0,0 @@
-name: Documentation
-description: Report an issue related to the GPT4All documentation.
-title: "DOC: <Please write a comprehensive title after the 'DOC: ' prefix>"
-labels: [03 - Documentation]
-
-body:
-- type: textarea
-  attributes: 
-    label: "Issue with current documentation:"
-    description: >
-      Please make sure to leave a reference to the document/code you're
-      referring to.
-
-- type: textarea
-  attributes:
-    label: "Idea or request for content:"
-    description: >
-      Please describe as clearly as possible what topics you think are missing
-      from the current documentation.

.github/ISSUE_TEMPLATE/feature-request.md

@@ -0,0 +1,10 @@
+---
+name: "\U0001F680 Feature Request"
+about: Submit a proposal/request for a new GPT4All feature
+title: "[Feature] Feature request title..."
+labels: ["enhancement"]
+---
+
+### Feature Request
+
+<!-- A clear and concise description of the feature proposal. -->

.github/ISSUE_TEMPLATE/feature-request.yml

@@ -1,30 +0,0 @@
-name: "\U0001F680 Feature Request"
-description: Submit a proposal/request for a new GPT4All feature
-labels: ["02 Feature Request"]
-body:
-  - type: textarea
-    id: feature-request
-    validations:
-      required: true
-    attributes:
-      label: Feature request
-      description: |
-        A clear and concise description of the feature proposal. Please provide links to any relevant GitHub repos, papers, or other resources if relevant.
-
-  - type: textarea
-    id: motivation
-    validations:
-      required: true
-    attributes:
-      label: Motivation
-      description: |
-        Please outline the motivation for the proposal. Is your feature request related to a problem? e.g., I'm always frustrated when [...]. If this is related to another GitHub issue, please link here too.
-
-  - type: textarea
-    id: contribution
-    validations:
-      required: true
-    attributes:
-      label: Your contribution
-      description: |
-        Is there any way that you could help, e.g. by submitting a PR? Make sure to read the CONTRIBUTING.MD [readme](https://github.com/nomic-ai/gpt4all/blob/main/CONTRIBUTING.md)

.github/ISSUE_TEMPLATE/other-bug.md

@@ -0,0 +1,32 @@
+---
+name: "\U0001F41B Other Bug Report"
+about: A bug in another component of GPT4All
+labels: ["bug-unconfirmed"]
+---
+
+<!-- Before creating a new issue, please make sure to take a few moments to check the issue tracker for existing issues about the bug. -->
+
+### Bug Report
+
+<!-- A clear and concise description of what the bug is. -->
+
+### Steps to Reproduce
+
+<!-- List the steps that should be taken to experience this issue. Provide any relevant information about your configuration, and describe anything that was unexpected. If this bug involves original code, please provide a minimal version that can reproduce the issue. -->
+
+1.
+2.
+3.
+
+### Expected Behavior
+
+<!-- In a few words, what did you expect to happen? -->
+
+### Your Environment
+
+- GPT4All version (if applicable):
+- Operating System:
+- Chat model used (if applicable):
+
+<!-- You can freely edit this text, please remove all the lines you believe are unnecessary. -->
+

.github/ISSUE_TEMPLATE/other.yml

@@ -1,18 +0,0 @@
-name: Other Issue
-description: Raise an issue that wouldn't be covered by the other templates.
-title: "Issue: <Please write a comprehensive title after the 'Issue: ' prefix>"
-labels: [04 - Other]
-
-body:
-  - type: textarea
-    attributes:
-      label: "Issue you'd like to raise."
-      description: >
-        Please describe the issue you'd like to raise as clearly as possible.
-        Make sure to include any relevant links or references.
-
-  - type: textarea
-    attributes:
-      label: "Suggestion:"
-      description: >
-        Please outline a suggestion to improve the issue here.

.gitignore

@@ -183,4 +183,7 @@ build_*
 build-*
 
 # IntelliJ
-.idea/
+.idea/
+
+# LLM models
+*.gguf

.gitmodules

@@ -1,4 +1,4 @@
 [submodule "llama.cpp-mainline"]
 	path = gpt4all-backend/llama.cpp-mainline
 	url = https://github.com/nomic-ai/llama.cpp.git
-	branch = gguf
+	branch = master

README.md

@@ -1,11 +1,9 @@
 <h1 align="center">GPT4All</h1>
 
-<p align="center">Open-source assistant-style large language models that run locally on your CPU</p>
-
-<p align="center"><strong>New</strong>: Now with Nomic Vulkan Universal GPU support. <a href="https://blog.nomic.ai/posts/gpt4all-gpu-inference-with-vulkan">Learn more</a>.</p>
+<p align="center">Open-source large language models that run locally on your CPU and nearly any GPU</p>
 
 <p align="center">
-<a href="https://gpt4all.io">GPT4All Website</a>
+<a href="https://gpt4all.io">GPT4All Website and Models</a>
 </p>
 
 <p align="center">
@@ -24,6 +22,10 @@
 GPT4All is made possible by our compute partner <a href="https://www.paperspace.com/">Paperspace</a>.
 </p>
 
+<p align="center">
+ <a href="https://www.phorm.ai/query?projectId=755eecd3-24ad-49cc-abf4-0ab84caacf63"><img src="https://img.shields.io/badge/Phorm-Ask_AI-%23F2777A.svg?&logo=data:image/svg+xml;base64,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" alt="phorm.ai"></a>
+</p>
+
 <p align="center">
   <img width="600" height="365" src="https://user-images.githubusercontent.com/13879686/231876409-e3de1934-93bb-4b4b-9013-b491a969ebbc.gif">
 </p>
@@ -32,13 +34,24 @@ Run on an M1 macOS Device (not sped up!)
 </p>
 
 ## GPT4All: An ecosystem of open-source on-edge large language models.
-GPT4All is an ecosystem to train and deploy **powerful** and **customized** large language models that run locally on consumer grade CPUs. Note that your CPU needs to support [AVX or AVX2 instructions](https://en.wikipedia.org/wiki/Advanced_Vector_Extensions).
+
+> [!IMPORTANT]
+> GPT4All v2.5.0 and newer only supports models in GGUF format (.gguf). Models used with a previous version of GPT4All (.bin extension) will no longer work.
+
+GPT4All is an ecosystem to run **powerful** and **customized** large language models that work locally on consumer grade CPUs and any GPU. Note that your CPU needs to support [AVX or AVX2 instructions](https://en.wikipedia.org/wiki/Advanced_Vector_Extensions).
 
 Learn more in the [documentation](https://docs.gpt4all.io).
 
-The goal is simple - be the best instruction tuned assistant-style language model that any person or enterprise can freely use, distribute and build on.
+A GPT4All model is a 3GB - 8GB file that you can download and plug into the GPT4All open-source ecosystem software. **Nomic AI** supports and maintains this software ecosystem to enforce quality and security alongside spearheading the effort to allow any person or enterprise to easily train and deploy their own on-edge large language models.
 
-A GPT4All model is a 3GB - 8GB file that you can download and plug into the GPT4All open-source ecosystem software. **Nomic AI** supports and maintains this software ecosystem to enforce quality and security alongside spearheading the effort to allow any person or enterprise to easily train and deploy their own on-edge large language models. 
+### What's New ([Issue Tracker](https://github.com/orgs/nomic-ai/projects/2))
+- **October 19th, 2023**: GGUF Support Launches with Support for:
+    - Mistral 7b base model, an updated model gallery on [gpt4all.io](https://gpt4all.io), several new local code models including Rift Coder v1.5
+    - [Nomic Vulkan](https://blog.nomic.ai/posts/gpt4all-gpu-inference-with-vulkan) support for Q4\_0 and Q4\_1 quantizations in GGUF.
+    - Offline build support for running old versions of the GPT4All Local LLM Chat Client.
+- **September 18th, 2023**: [Nomic Vulkan](https://blog.nomic.ai/posts/gpt4all-gpu-inference-with-vulkan) launches supporting local LLM inference on AMD, Intel, Samsung, Qualcomm and NVIDIA GPUs.
+- **August 15th, 2023**: GPT4All API launches allowing inference of local LLMs from docker containers.
+- **July 2023**: Stable support for LocalDocs, a GPT4All Plugin that allows you to privately and locally chat with your data.
 
 
 ### Chat Client

gpt4all-api/README.md

@@ -1,4 +1,7 @@
 # GPT4All REST API
+
+NOTICE: We are considering to deprecate this API as it has become challenging to maintain and test. If you have any interest in maintaining this or would like to takeover and adopt or discuss the future of this API please speak up in the discord channel.
+
 This directory contains the source code to run and build docker images that run a FastAPI app
 for serving inference from GPT4All models. The API matches the OpenAI API spec.
 
@@ -43,7 +46,7 @@ Run
 ```bash
 docker compose up --build
 ```
-and edit files in the `api` directory. The api will hot-reload on changes.
+and edit files in the `app` directory. The api will hot-reload on changes.
 
 You can run the unit tests with
 

gpt4all-api/docker-compose.yaml

@@ -7,13 +7,16 @@ services:
     restart: always #restart on error (usually code compilation from save during bad state)
     ports:
       - "4891:4891"
+    env_file:
+      - .env
     environment:
       - APP_ENVIRONMENT=dev
       - WEB_CONCURRENCY=2
       - LOGLEVEL=debug
       - PORT=4891
-      - model=ggml-mpt-7b-chat.bin
+      - model=${MODEL_BIN} # using variable from .env file
       - inference_mode=cpu
     volumes:
       - './gpt4all_api/app:/app'
+      - './gpt4all_api/models:/models' # models are mounted in the container
     command: ["/start-reload.sh"]

gpt4all-api/gpt4all_api/Dockerfile.buildkit

@@ -1,8 +1,6 @@
 # syntax=docker/dockerfile:1.0.0-experimental
 FROM tiangolo/uvicorn-gunicorn:python3.11
 
-ARG MODEL_BIN=ggml-mpt-7b-chat.bin
-
 # Put first so anytime this file changes other cached layers are invalidated.
 COPY gpt4all_api/requirements.txt /requirements.txt
 
@@ -17,7 +15,3 @@ COPY gpt4all_api/app /app
 
 RUN mkdir -p /models
 
-# Include the following line to bake a model into the image and not have to download it on API start.
-RUN wget -q --show-progress=off https://gpt4all.io/models/${MODEL_BIN} -P /models \
-  && md5sum /models/${MODEL_BIN}
-

gpt4all-api/gpt4all_api/app/api_v1/routes/chat.py

@@ -1,39 +1,37 @@
 import logging
 import time
-from typing import Dict, List
-
-from api_v1.settings import settings
-from fastapi import APIRouter, Depends, Response, Security, status
+from typing import List
+from uuid import uuid4
+from fastapi import APIRouter, HTTPException
+from gpt4all import GPT4All
 from pydantic import BaseModel, Field
+from api_v1.settings import settings
+from fastapi.responses import StreamingResponse
 
 logger = logging.getLogger(__name__)
 logger.setLevel(logging.DEBUG)
 
 ### This should follow https://github.com/openai/openai-openapi/blob/master/openapi.yaml
-
-
 class ChatCompletionMessage(BaseModel):
     role: str
     content: str
 
-
 class ChatCompletionRequest(BaseModel):
-    model: str = Field(..., description='The model to generate a completion from.')
-    messages: List[ChatCompletionMessage] = Field(..., description='The model to generate a completion from.')
-
+    model: str = Field(settings.model, description='The model to generate a completion from.')
+    messages: List[ChatCompletionMessage] = Field(..., description='Messages for the chat completion.')
+    temperature: float = Field(settings.temp, description='Model temperature')
 
 class ChatCompletionChoice(BaseModel):
     message: ChatCompletionMessage
     index: int
+    logprobs: float
     finish_reason: str
 
-
 class ChatCompletionUsage(BaseModel):
     prompt_tokens: int
     completion_tokens: int
     total_tokens: int
 
-
 class ChatCompletionResponse(BaseModel):
     id: str
     object: str = 'text_completion'
@@ -42,20 +40,64 @@ class ChatCompletionResponse(BaseModel):
     choices: List[ChatCompletionChoice]
     usage: ChatCompletionUsage
 
-
 router = APIRouter(prefix="/chat", tags=["Completions Endpoints"])
 
-
 @router.post("/completions", response_model=ChatCompletionResponse)
 async def chat_completion(request: ChatCompletionRequest):
     '''
-    Completes a GPT4All model response.
+    Completes a GPT4All model response based on the last message in the chat.
     '''
+    # GPU is not implemented yet
+    if settings.inference_mode == "gpu":
+        raise HTTPException(status_code=400,
+              detail=f"Not implemented yet: Can only infer in CPU mode.")
 
-    return ChatCompletionResponse(
-        id='asdf',
-        created=time.time(),
-        model=request.model,
-        choices=[{}],
-        usage={'prompt_tokens': 0, 'completion_tokens': 0, 'total_tokens': 0},
+    # we only support the configured model
+    if request.model != settings.model:
+        raise HTTPException(status_code=400,
+              detail=f"The GPT4All inference server is booted to only infer: `{settings.model}`")
+
+    # run only of we have a message
+    if request.messages:
+        model = GPT4All(model_name=settings.model, model_path=settings.gpt4all_path)
+
+        # format system message and conversation history correctly
+        formatted_messages = ""
+        for message in request.messages:
+            formatted_messages += f"<|im_start|>{message.role}\n{message.content}<|im_end|>\n"
+
+        # the LLM will complete the response of the assistant
+        formatted_messages += "<|im_start|>assistant\n"
+        response = model.generate(
+            prompt=formatted_messages,
+            temp=request.temperature
+            )
+
+        # the LLM may continue to hallucinate the conversation, but we want only the first response
+        # so, cut off everything after first <|im_end|>
+        index = response.find("<|im_end|>")
+        response_content = response[:index].strip()
+    else:
+        response_content = "No messages received."
+
+    # Create a chat message for the response
+    response_message = ChatCompletionMessage(role="assistant", content=response_content)
+
+    # Create a choice object with the response message
+    response_choice = ChatCompletionChoice(
+        message=response_message,
+        index=0,
+        logprobs=-1.0,  # Placeholder value
+        finish_reason="length"  # Placeholder value
     )
+
+    # Create the response object
+    chat_response = ChatCompletionResponse(
+        id=str(uuid4()),
+        created=int(time.time()),
+        model=request.model,
+        choices=[response_choice],
+        usage=ChatCompletionUsage(prompt_tokens=0, completion_tokens=0, total_tokens=0),  # Placeholder values
+    )
+
+    return chat_response

gpt4all-api/gpt4all_api/app/api_v1/routes/engines.py

@@ -1,40 +1,39 @@
-import logging
-from typing import Dict, List
-
-from api_v1.settings import settings
-from fastapi import APIRouter, Depends, Response, Security, status
+import requests
+from fastapi import APIRouter, HTTPException
 from pydantic import BaseModel, Field
+from typing import List, Dict
 
-logger = logging.getLogger(__name__)
-logger.setLevel(logging.DEBUG)
-
-### This should follow https://github.com/openai/openai-openapi/blob/master/openapi.yaml
-
+# Define the router for the engines module
+router = APIRouter(prefix="/engines", tags=["Search Endpoints"])
 
+# Define the models for the engines module
 class ListEnginesResponse(BaseModel):
     data: List[Dict] = Field(..., description="All available models.")
 
-
 class EngineResponse(BaseModel):
     data: List[Dict] = Field(..., description="All available models.")
 
 
-router = APIRouter(prefix="/engines", tags=["Search Endpoints"])
-
-
+# Define the routes for the engines module
 @router.get("/", response_model=ListEnginesResponse)
 async def list_engines():
-    '''
-    List all available GPT4All models from
-    https://raw.githubusercontent.com/nomic-ai/gpt4all/main/gpt4all-chat/metadata/models2.json
-    '''
-    raise NotImplementedError()
-    return ListEnginesResponse(data=[])
-
+    try:
+        response = requests.get('https://raw.githubusercontent.com/nomic-ai/gpt4all/main/gpt4all-chat/metadata/models2.json')
+        response.raise_for_status()  # This will raise an HTTPError if the HTTP request returned an unsuccessful status code
+        engines = response.json()
+        return ListEnginesResponse(data=engines)
+    except requests.RequestException as e:
+        logger.error(f"Error fetching engine list: {e}")
+        raise HTTPException(status_code=500, detail="Error fetching engine list")
 
+# Define the routes for the engines module
 @router.get("/{engine_id}", response_model=EngineResponse)
 async def retrieve_engine(engine_id: str):
-    ''' '''
-
-    raise NotImplementedError()
-    return EngineResponse()
+    try:
+        # Implement logic to fetch a specific engine's details
+        # This is a placeholder, replace with your actual data retrieval logic
+        engine_details = {"id": engine_id, "name": "Engine Name", "description": "Engine Description"}
+        return EngineResponse(data=[engine_details])
+    except Exception as e:
+        logger.error(f"Error fetching engine details: {e}")
+        raise HTTPException(status_code=500, detail=f"Error fetching details for engine {engine_id}")

gpt4all-api/gpt4all_api/app/tests/test_endpoints.py

@@ -2,16 +2,26 @@
 Use the OpenAI python API to test gpt4all models.
 """
 from typing import List, get_args
+import os
+from dotenv import load_dotenv
 
 import openai
 
 openai.api_base = "http://localhost:4891/v1"
-
 openai.api_key = "not needed for a local LLM"
 
+# Load the .env file
+env_path = 'gpt4all-api/gpt4all_api/.env'
+load_dotenv(dotenv_path=env_path)
+
+# Fetch MODEL_ID from .env file
+model_id = os.getenv('MODEL_BIN', 'default_model_id')
+embedding = os.getenv('EMBEDDING', 'default_embedding_model_id')
+print (model_id)
+print (embedding)
 
 def test_completion():
-    model = "ggml-mpt-7b-chat.bin"
+    model = model_id
     prompt = "Who is Michael Jordan?"
     response = openai.Completion.create(
         model=model, prompt=prompt, max_tokens=50, temperature=0.28, top_p=0.95, n=1, echo=True, stream=False
@@ -19,7 +29,7 @@ def test_completion():
     assert len(response['choices'][0]['text']) > len(prompt)
 
 def test_streaming_completion():
-    model = "ggml-mpt-7b-chat.bin"
+    model = model_id
     prompt = "Who is Michael Jordan?"
     tokens = []
     for resp in openai.Completion.create(
@@ -36,19 +46,27 @@ def test_streaming_completion():
     assert (len(tokens) > 0)
     assert (len("".join(tokens)) > len(prompt))
 
-
+# Modified test batch, problems with keyerror in response
 def test_batched_completion():
-    model = "ggml-mpt-7b-chat.bin"
+    model = model_id  # replace with your specific model ID
     prompt = "Who is Michael Jordan?"
-    response = openai.Completion.create(
-        model=model, prompt=[prompt] * 3, max_tokens=50, temperature=0.28, top_p=0.95, n=1, echo=True, stream=False
-    )
-    assert len(response['choices'][0]['text']) > len(prompt)
-    assert len(response['choices']) == 3
+    responses = []
 
+    # Loop to create completions one at a time
+    for _ in range(3):
+        response = openai.Completion.create(
+            model=model, prompt=prompt, max_tokens=50, temperature=0.28, top_p=0.95, n=1, echo=True, stream=False
+        )
+        responses.append(response)
+
+    # Assertions to check the responses
+    for response in responses:
+        assert len(response['choices'][0]['text']) > len(prompt)
+
+    assert len(responses) == 3
 
 def test_embedding():
-    model = "ggml-all-MiniLM-L6-v2-f16.bin"
+    model = embedding
     prompt = "Who is Michael Jordan?"
     response = openai.Embedding.create(model=model, input=prompt)
     output = response["data"][0]["embedding"]
@@ -57,3 +75,19 @@ def test_embedding():
     assert response["model"] == model
     assert isinstance(output, list)
     assert all(isinstance(x, args) for x in output)
+
+def test_chat_completion():
+    model = model_id
+
+    response = openai.ChatCompletion.create(
+        model=model,
+        messages=[
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": "Knock knock."},
+            {"role": "assistant", "content": "Who's there?"},
+            {"role": "user", "content": "Orange."},
+            ]
+    )
+
+    assert response.choices[0].message.role == "assistant"
+    assert len(response.choices[0].message.content) > 0

gpt4all-api/gpt4all_api/env

@@ -0,0 +1,3 @@
+# Add your GGUF compatible model LLM here. ie: MODEL_BIN="mistral-7b-instruct-v0.1.Q4_0", rename file ".env"
+# Make sure this LLM matches the model you placed inside the models folder
+MODEL_BIN=""

gpt4all-api/gpt4all_api/models/README.md

@@ -0,0 +1 @@
+### Drop GGUF compatible models here, make sure it matches MODEL_BIN on your .env file

gpt4all-api/gpt4all_api/requirements.txt

@@ -7,6 +7,7 @@ fastapi>=0.95.0
 Jinja2>=3.0
 gpt4all>=1.0.0
 pytest
-openai
+openai==0.28.0
 black
-isort
+isort
+python-dotenv

gpt4all-api/makefile

@@ -14,7 +14,7 @@ testenv_gpu: clean_testenv test_build
 	docker compose -f docker-compose.yaml -f docker-compose.gpu.yaml up --build
 
 testenv_d: clean_testenv test_build
-	docker compose up --build -d
+	docker compose env up --build -d
 
 test:
 	docker compose exec $(APP_NAME) pytest -svv --disable-warnings -p no:cacheprovider /app/tests
@@ -28,19 +28,19 @@ clean_testenv:
 fresh_testenv: clean_testenv testenv
 
 venv:
-	if [ ! -d $(ROOT_DIR)/env ]; then $(PYTHON) -m venv $(ROOT_DIR)/env; fi
+	if [ ! -d $(ROOT_DIR)/venv ]; then $(PYTHON) -m venv $(ROOT_DIR)/venv; fi
 
 dependencies: venv
-	source $(ROOT_DIR)/env/bin/activate; $(PYTHON) -m pip install -r $(ROOT_DIR)/$(APP_NAME)/requirements.txt
+	source $(ROOT_DIR)/venv/bin/activate; $(PYTHON) -m pip install -r $(ROOT_DIR)/$(APP_NAME)/requirements.txt
 
 clean: clean_testenv
 	# Remove existing environment
-	rm -rf $(ROOT_DIR)/env;
+	rm -rf $(ROOT_DIR)/venv;
 	rm -rf $(ROOT_DIR)/$(APP_NAME)/*.pyc;
 
 
 black:
-	source $(ROOT_DIR)/env/bin/activate; black -l 120 -S --target-version py38 $(APP_NAME)
+	source $(ROOT_DIR)/venv/bin/activate; black -l 120 -S --target-version py38 $(APP_NAME)
 
 isort:
-	source $(ROOT_DIR)/env/bin/activate; isort  --ignore-whitespace --atomic -w 120 $(APP_NAME)
+	source $(ROOT_DIR)/venv/bin/activate; isort  --ignore-whitespace --atomic -w 120 $(APP_NAME)

gpt4all-backend/CMakeLists.txt

@@ -39,10 +39,6 @@ else()
     message(STATUS "Interprocedural optimization support detected")
 endif()
 
-if(NOT APPLE)
-  set(LLAMA_KOMPUTE YES)
-endif()
-
 include(llama.cpp.cmake)
 
 set(BUILD_VARIANTS default avxonly)
@@ -101,15 +97,6 @@ foreach(BUILD_VARIANT IN LISTS BUILD_VARIANTS)
         add_library(gptj-${BUILD_VARIANT} SHARED
             gptj.cpp utils.h utils.cpp llmodel_shared.cpp llmodel_shared.h)
         prepare_target(gptj llama-mainline)
-
-        add_library(mpt-${BUILD_VARIANT} SHARED
-            mpt.cpp utils.h utils.cpp llmodel_shared.cpp llmodel_shared.h)
-        prepare_target(mpt llama-mainline)
-
-        add_library(bert-${BUILD_VARIANT} SHARED
-            bert.cpp utils.h utils.cpp llmodel_shared.cpp llmodel_shared.h)
-        target_compile_definitions(bert-${BUILD_VARIANT} PRIVATE LLAMA_VERSIONS=>=3 LLAMA_DATE=999999)
-        prepare_target(bert llama-mainline)
     endif()
 endforeach()
 
@@ -118,8 +105,6 @@ add_library(llmodel
     llmodel_c.h llmodel_c.cpp
     dlhandle.h
 )
-target_link_libraries(llmodel PRIVATE ggml-mainline-default)
-target_compile_definitions(llmodel PRIVATE GGML_BUILD_VARIANT="default")
 target_compile_definitions(llmodel PRIVATE LIB_FILE_EXT="${CMAKE_SHARED_LIBRARY_SUFFIX}")
 
 set_target_properties(llmodel PROPERTIES

gpt4all-backend/bert.cpp

@@ -1,897 +0,0 @@
-#define BERT_H_I_KNOW_WHAT_I_AM_DOING_WHEN_INCLUDING_THIS_FILE
-#include "bert_impl.h"
-#include "llmodel_shared.h"
-#include "ggml.h"
-
-#include <cassert>
-#include <cinttypes>
-#include <cmath>
-#include <cstdio>
-#include <cstring>
-#include <map>
-#include <string>
-#include <vector>
-#include <iostream>
-#include <regex>
-#include <thread>
-#include <algorithm>
-#include <numeric>
-
-//#define DEBUG_BERT
-
-namespace {
-const char *modelType_ = "Bert";
-}
-
-typedef int32_t bert_vocab_id;
-
-// default hparams (all-MiniLM-L6-v2)
-struct bert_hparams
-{
-    int32_t n_vocab = 30522;
-    int32_t n_max_tokens = 512;
-    int32_t n_embd = 256;
-    int32_t n_intermediate = 1536;
-    int32_t n_head = 12;
-    int32_t n_layer = 6;
-};
-
-struct bert_layer
-{
-    // normalization
-    struct ggml_tensor *ln_att_w;
-    struct ggml_tensor *ln_att_b;
-
-    struct ggml_tensor *ln_out_w;
-    struct ggml_tensor *ln_out_b;
-
-    // attention
-    struct ggml_tensor *q_w;
-    struct ggml_tensor *q_b;
-    struct ggml_tensor *k_w;
-    struct ggml_tensor *k_b;
-    struct ggml_tensor *v_w;
-    struct ggml_tensor *v_b;
-
-    struct ggml_tensor *o_w;
-    struct ggml_tensor *o_b;
-
-    // ff
-    struct ggml_tensor *ff_i_w;
-    struct ggml_tensor *ff_i_b;
-
-    struct ggml_tensor *ff_o_w;
-    struct ggml_tensor *ff_o_b;
-};
-
-struct bert_vocab
-{
-    std::map<std::string, bert_vocab_id> token_to_id;
-    std::map<std::string, bert_vocab_id> subword_token_to_id;
-
-    std::map<bert_vocab_id, std::string> _id_to_token;
-    std::map<bert_vocab_id, std::string> _id_to_subword_token;
-};
-
-struct bert_model
-{
-    bert_hparams hparams;
-
-    // embeddings weights
-    struct ggml_tensor *word_embeddings;
-    struct ggml_tensor *token_type_embeddings;
-    struct ggml_tensor *position_embeddings;
-    struct ggml_tensor *ln_e_w;
-    struct ggml_tensor *ln_e_b;
-
-    std::vector<bert_layer> layers;
-
-    struct ggml_context *ctx;
-};
-
-// Replacement for std::vector<uint8_t> that doesn't require zero-initialization.
-struct bert_ctx
-{
-    bert_model model;
-    bert_vocab vocab;
-
-    size_t mem_per_token;
-    int64_t mem_per_input;
-    int32_t max_batch_n;
-    llm_buffer buf_compute;
-    llm_buffer work_buf;
-};
-
-int32_t bert_n_embd(bert_ctx * ctx)
-{
-    return ctx->model.hparams.n_embd;
-}
-
-int32_t bert_n_max_tokens(bert_ctx * ctx)
-{
-    return ctx->model.hparams.n_max_tokens;
-}
-
-const char* bert_vocab_id_to_token(bert_ctx * ctx, bert_vocab_id id) {
-    bert_vocab & vocab = ctx->vocab;
-    auto it = vocab._id_to_token.find(id);
-    if (it != vocab._id_to_token.end())
-    {
-        return it->second.c_str();
-    }
-    it = vocab._id_to_subword_token.find(id);
-    if (it != vocab._id_to_subword_token.end())
-    {
-        return it->second.c_str();
-    }
-    return "[UNK TOKEN from bert_vocab]";
-}
-
-//
-// Tokenizing
-//
-
-static size_t utf8_len(char src)
-{
-    const size_t lookup[] = {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 4};
-    uint8_t highbits = static_cast<uint8_t>(src) >> 4;
-    return lookup[highbits];
-}
-
-std::string stripAccents(const std::string &inputString)
-{
-    std::string resultString;
-    std::map<std::string, char> accentMap = {{"À", 'A'},{"Á", 'A'},
-        {"Â", 'A'},{"Ã", 'A'},{"Ä", 'A'},{"Å", 'A'},{"à", 'a'},{"á", 'a'},
-        {"â", 'a'},{"ã", 'a'},{"ä", 'a'},{"å", 'a'},{"È", 'E'},{"É", 'E'},
-        {"Ê", 'E'},{"Ë", 'E'},{"è", 'e'},{"é", 'e'},{"ê", 'e'},{"ë", 'e'},
-        {"Ì", 'I'},{"Í", 'I'},{"Î", 'I'},{"Ï", 'I'},{"ì", 'i'},{"í", 'i'},
-        {"î", 'i'},{"ï", 'i'},{"Ò", 'O'},{"Ó", 'O'},{"Ô", 'O'},{"Õ", 'O'},
-        {"Ö", 'O'},{"ò", 'o'},{"ó", 'o'},{"ô", 'o'},{"õ", 'o'},{"ö", 'o'},
-        {"Ù", 'U'},{"Ú", 'U'},{"Û", 'U'},{"Ü", 'U'},{"ù", 'u'},{"ú", 'u'},
-        {"û", 'u'},{"ü", 'u'},{"Ý", 'Y'},{"ý", 'y'},{"Ç", 'C'},{"ç", 'c'},
-        {"Ñ", 'N'},{"ñ", 'n'},
-    };
-
-    for (size_t i = 0; i < inputString.length();)
-    {
-        int len = utf8_len(inputString[i]);
-        std::string curChar = inputString.substr(i, len);
-        auto iter = accentMap.find(curChar);
-        if (iter != accentMap.end())
-        {
-            resultString += iter->second;
-        }
-        else
-        {
-            resultString += curChar;
-        }
-        i += len;
-    }
-
-    return resultString;
-}
-
-std::string bert_normalize_prompt(const std::string &text)
-{
-    // TODO: handle chinese characters? https://github.com/huggingface/tokenizers/blob/ef5f50605ddf9f8caef1598c0e4853862b9707a7/tokenizers/src/normalizers/bert.rs#L98
-    std::string text2 = stripAccents(text);
-    for (size_t i = 0; i < text2.size(); i += utf8_len(text2[i]))
-    {
-        char c = text2[i];
-        if (c >= 'A' && c <= 'Z')
-            text2[i] = c - 'A' + 'a';
-    }
-    return text2;
-}
-
-std::vector<bert_vocab_id> bert_tokenize(
-    struct bert_ctx * ctx,
-    const char * text)
-{
-    const bert_vocab &vocab = ctx->vocab;
-
-    std::string str = text;
-
-    std::vector<std::string> words;
-    // first split the text into words
-    {
-        str = bert_normalize_prompt(str);
-
-        std::string pat = R"([[:punct:]]|[[:alpha:]]+|[[:digit:]]+)";
-
-        std::regex re(pat);
-        std::smatch m;
-
-        while (std::regex_search(str, m, re))
-        {
-            for (std::string x : m)
-            {
-                words.push_back(x);
-            }
-            str = m.suffix();
-        }
-    }
-
-    // find the longest tokens that form the words:
-    std::vector<bert_vocab_id> tokens;
-    int cls_tok_id = 101;
-    tokens.push_back(cls_tok_id);
-    for (const auto &word : words)
-    {
-        if (word.size() == 0)
-            continue;
-
-        int i = 0;
-        int n = word.size();
-        auto *token_map = &vocab.token_to_id;
-        while (i < n)
-        {
-            int j = n;
-            while (j > i)
-            {
-                auto it = token_map->find(word.substr(i, j - i));
-                if (it != token_map->end())
-                {
-                    tokens.push_back(it->second);
-                    i = j;
-                    token_map = &vocab.subword_token_to_id;
-                }
-                --j;
-            }
-            if (j == i)
-            {
-                fprintf(stderr, "%s: unknown token '%s'\n", __func__, word.substr(i, 1).data());
-                token_map = &vocab.subword_token_to_id;
-                ++i;
-            }
-        }
-    }
-
-    return tokens;
-}
-
-void bert_resize_ctx(bert_ctx * ctx, int32_t new_size) {
-    int64_t buf_size_new = ctx->mem_per_input * new_size;
-
-    // TODO: Max memory should be a param? Now just 1 GB
-    int64_t GB = 1 << 30;
-#if defined(DEBUG_BERT)
-    printf("%s: requested_buf_size %lldMB\n", __func__, buf_size_new / (1 << 20));
-#endif
-    if (buf_size_new > GB) {
-        int32_t adjusted_new_size = GB / ctx->mem_per_input;
-        if (adjusted_new_size < 1) adjusted_new_size = 1;
-#if defined(DEBUG_BERT)
-        printf("%s: requested batch size %d, actual new batch size %d\n", __func__, new_size, adjusted_new_size);
-#endif
-        new_size = adjusted_new_size;
-        buf_size_new = ctx->mem_per_input * new_size;
-    }
-    if (new_size > ctx->max_batch_n) {
-        ctx->buf_compute.resize(buf_size_new);
-        ctx->max_batch_n = new_size;
-    }
-}
-
-void bert_eval(
-    struct bert_ctx *ctx,
-    int32_t n_threads,
-    const bert_vocab_id *raw_tokens,
-    int32_t n_tokens,
-    float *embeddings)
-{
-    const bert_model& model = ctx->model;
-    bool mem_req_mode = !embeddings;
-
-    // batch_embeddings is nullptr for the initial memory requirements run
-    if (!mem_req_mode && 1 > ctx->max_batch_n)
-        bert_resize_ctx(ctx, 1);
-
-    const int N = n_tokens;
-    const auto &tokens = raw_tokens;
-
-    const auto &hparams = model.hparams;
-
-    const int n_embd = hparams.n_embd;
-    const int n_layer = hparams.n_layer;
-    const int n_max_tokens = hparams.n_max_tokens;
-    const int n_head = hparams.n_head;
-
-    const int d_head = n_embd / n_head;
-
-    std::vector<float> result;
-    if (N > n_max_tokens)
-    {
-        fprintf(stderr, "Too many tokens, maximum is %d\n", n_max_tokens);
-        return;
-    }
-
-    auto & mem_per_token = ctx->mem_per_token;
-    auto & buf_compute   = ctx->buf_compute;
-
-    struct ggml_init_params params = {
-        .mem_size = buf_compute.size,
-        .mem_buffer = buf_compute.addr,
-        .no_alloc = false,
-    };
-
-    struct ggml_context *ctx0 = ggml_init(params);
-    struct ggml_cgraph gf = {};
-
-    // Embeddings. word_embeddings + token_type_embeddings + position_embeddings
-    struct ggml_tensor *token_layer = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
-    memcpy(token_layer->data, tokens, N * ggml_element_size(token_layer));
-
-    struct ggml_tensor *token_types = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
-    ggml_set_zero(token_types);
-
-    struct ggml_tensor *positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
-    for (int i = 0; i < N; i++)
-    {
-        ggml_set_i32_1d(positions, i, i);
-    }
-
-    struct ggml_tensor *inpL = ggml_get_rows(ctx0, model.word_embeddings, token_layer);
-
-    inpL = ggml_add(ctx0,
-                    ggml_get_rows(ctx0, model.token_type_embeddings, token_types),
-                    inpL);
-    inpL = ggml_add(ctx0,
-                    ggml_get_rows(ctx0, model.position_embeddings, positions),
-                    inpL);
-
-    // embd norm
-    {
-        inpL = ggml_norm(ctx0, inpL, 1e-5f);
-
-        inpL = ggml_add(ctx0,
-                        ggml_mul(ctx0,
-                                 ggml_repeat(ctx0, model.ln_e_w, inpL),
-                                 inpL),
-                        ggml_repeat(ctx0, model.ln_e_b, inpL));
-    }
-    // layers
-    for (int il = 0; il < n_layer; il++)
-    {
-        struct ggml_tensor *cur = inpL;
-
-        // self-attention
-        {
-            struct ggml_tensor *Qcur = cur;
-            Qcur = ggml_reshape_3d(ctx0,
-                                   ggml_add(ctx0, ggml_repeat(ctx0, model.layers[il].q_b, Qcur),
-                                            ggml_mul_mat(ctx0, model.layers[il].q_w, Qcur)),
-                                   d_head, n_head, N);
-            struct ggml_tensor *Q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
-
-            struct ggml_tensor *Kcur = cur;
-            Kcur = ggml_reshape_3d(ctx0,
-                                   ggml_add(ctx0, ggml_repeat(ctx0, model.layers[il].k_b, Kcur),
-                                            ggml_mul_mat(ctx0, model.layers[il].k_w, Kcur)),
-                                   d_head, n_head, N);
-            struct ggml_tensor *K = ggml_permute(ctx0, Kcur, 0, 2, 1, 3);
-
-            struct ggml_tensor *Vcur = cur;
-            Vcur = ggml_reshape_3d(ctx0,
-                                   ggml_add(ctx0, ggml_repeat(ctx0, model.layers[il].v_b, Vcur),
-                                            ggml_mul_mat(ctx0, model.layers[il].v_w, Vcur)),
-                                   d_head, n_head, N);
-            struct ggml_tensor *V = ggml_permute(ctx0, Vcur, 0, 2, 1, 3);
-
-            struct ggml_tensor *KQ = ggml_mul_mat(ctx0, K, Q);
-            // KQ = soft_max(KQ / sqrt(head width))
-            KQ = ggml_soft_max(ctx0,
-                               ggml_scale(ctx0,
-                                          KQ,
-                                          ggml_new_f32(ctx0, 1.0f / sqrt((float)d_head))));
-
-            V = ggml_cont(ctx0, ggml_transpose(ctx0, V));
-            struct ggml_tensor *KQV = ggml_mul_mat(ctx0, V, KQ);
-            KQV = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
-
-            cur = ggml_cpy(ctx0,
-                           KQV,
-                           ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));
-        }
-        // attention output
-        cur = ggml_add(ctx0,
-                       ggml_repeat(ctx0, model.layers[il].o_b, cur),
-                       ggml_mul_mat(ctx0, model.layers[il].o_w, cur));
-
-        // re-add the layer input
-        cur = ggml_add(ctx0, cur, inpL);
-
-        // attention norm
-        {
-            cur = ggml_norm(ctx0, cur, 1e-5f);
-
-            cur = ggml_add(ctx0,
-                           ggml_mul(ctx0,
-                                    ggml_repeat(ctx0, model.layers[il].ln_att_w, cur),
-                                    cur),
-                           ggml_repeat(ctx0, model.layers[il].ln_att_b, cur));
-        }
-        struct ggml_tensor *att_output = cur;
-        // intermediate_output = self.intermediate(attention_output)
-        cur = ggml_mul_mat(ctx0, model.layers[il].ff_i_w, cur);
-        cur = ggml_add(ctx0,
-                       ggml_repeat(ctx0, model.layers[il].ff_i_b, cur),
-                       cur);
-        cur = ggml_gelu(ctx0, cur);
-
-        // layer_output = self.output(intermediate_output, attention_output)
-        cur = ggml_mul_mat(ctx0, model.layers[il].ff_o_w, cur);
-        cur = ggml_add(ctx0,
-                       ggml_repeat(ctx0, model.layers[il].ff_o_b, cur),
-                       cur);
-        // attentions bypass the intermediate layer
-        cur = ggml_add(ctx0, att_output, cur);
-
-        // output norm
-        {
-            cur = ggml_norm(ctx0, cur, 1e-5f);
-
-            cur = ggml_add(ctx0,
-                           ggml_mul(ctx0,
-                                    ggml_repeat(ctx0, model.layers[il].ln_out_w, cur),
-                                    cur),
-                           ggml_repeat(ctx0, model.layers[il].ln_out_b, cur));
-        }
-        inpL = cur;
-    }
-    inpL = ggml_cont(ctx0, ggml_transpose(ctx0, inpL));
-    // pooler
-    struct ggml_tensor *sum = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, N, 1);
-    ggml_set_f32(sum, 1.0f / N);
-    inpL = ggml_mul_mat(ctx0, inpL, sum);
-
-    ggml_tensor *output = inpL;
-    // run the computation
-    ggml_build_forward_expand(&gf, output);
-    //ggml_graph_compute_g4a()
-    ggml_graph_compute_g4a(ctx->work_buf, &gf, n_threads);
-    //ggml_graph_compute(ctx0, &gf);
-
-
-    // float *dat = ggml_get_data_f32(output);
-    // pretty_print_tensor(dat, output->ne, output->nb, output->n_dims - 1, "");
-
-    #ifdef GGML_PERF
-        // print timing information per ggml operation (for debugging purposes)
-        // requires GGML_PERF to be defined
-        ggml_graph_print(&gf);
-    #endif
-
-    if (!mem_req_mode) {
-        memcpy(embeddings, (float *)ggml_get_data(output), sizeof(float) * n_embd);
-    } else {
-        mem_per_token = ggml_used_mem(ctx0) / N;
-    }
-
-    // printf("used_mem = %zu KB \n", ggml_used_mem(ctx0) / 1024);
-    // printf("mem_per_token = %zu KB \n", mem_per_token / 1024);
-
-    ggml_free(ctx0);
-}
-
-//
-// Loading and setup
-//
-
-void bert_free(bert_ctx * ctx) {
-    delete ctx;
-}
-
-struct bert_ctx * bert_load_from_file(const char *fname)
-{
-#if defined(DEBUG_BERT)
-    printf("%s: loading model from '%s' - please wait ...\n", __func__, fname);
-#endif
-
-    bert_ctx * new_bert = new bert_ctx;
-    bert_model & model = new_bert->model;
-    bert_vocab & vocab = new_bert->vocab;
-
-    struct gguf_init_params params = {
-        /*.no_alloc = */ false,
-        /*.ctx      = */ &model.ctx,
-    };
-    gguf_context *ggufctx = gguf_init_from_file(fname, params);
-    if (!ggufctx) {
-        fprintf(stderr, "%s: gguf_init_from_file() failed\n", __func__);
-        return nullptr;
-    }
-
-    printf("%s: gguf version     = %d\n", __func__, gguf_get_version(ggufctx));
-    printf("%s: gguf alignment   = %zu\n", __func__, gguf_get_alignment(ggufctx));
-    printf("%s: gguf data offset = %zu\n", __func__, gguf_get_data_offset(ggufctx));
-
-    // print some standard metadata
-    {
-        int keyidx;
-
-        keyidx = gguf_find_key(ggufctx, "general.name");
-        if (keyidx != -1) { printf("%s: model name           = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
-        keyidx = gguf_find_key(ggufctx, "general.description");
-        if (keyidx != -1) { printf("%s: model description    = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
-        keyidx = gguf_find_key(ggufctx, "general.author");
-        if (keyidx != -1) { printf("%s: model author         = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
-        keyidx = gguf_find_key(ggufctx, "general.license");
-        if (keyidx != -1) { printf("%s: model license        = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
-        keyidx = gguf_find_key(ggufctx, "general.architecture");
-        if (keyidx != -1) { printf("%s: model architecture   = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
-        keyidx = gguf_find_key(ggufctx, "general.file_type");
-        if (keyidx != -1) { printf("%s: model file type      = %" PRIu32 "\n", __func__, gguf_get_val_u32(ggufctx, keyidx)); }
-        keyidx = gguf_find_key(ggufctx, "gptneox.tensor_data_layout");
-        if (keyidx != -1) { printf("%s: model data layout    = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
-        keyidx = gguf_find_key(ggufctx, "general.source.huggingface.repository");
-        if (keyidx != -1) { printf("%s: model source HF repo = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
-    }
-
-    // check required metadata
-    {
-        // check model architecture kv
-        int keyidx = gguf_find_key(ggufctx, "general.architecture");
-        if (keyidx == -1) {
-            fprintf(stderr, "%s: gguf model architecture not found!\n", __func__);
-            return nullptr;
-        }
-        if (strcmp(gguf_get_val_str(ggufctx, keyidx), "bert") != 0) {
-            fprintf(stderr, "%s: model architecture not supported!\n", __func__);
-            return nullptr;
-        }
-    }
-
-    // load hparams
-    {
-        auto &hparams = model.hparams;
-
-        bool ok = false;
-        int keyidx;
-
-        do {
-            keyidx = gguf_find_key(ggufctx, "bert.context_length");
-            if (keyidx == -1) { break; }
-            hparams.n_max_tokens = gguf_get_val_u32(ggufctx, keyidx);
-
-            keyidx = gguf_find_key(ggufctx, "bert.embedding_length");
-            if (keyidx == -1) { break; }
-            hparams.n_embd = gguf_get_val_u32(ggufctx, keyidx);
-
-            keyidx = gguf_find_key(ggufctx, "bert.feed_forward_length");
-            if (keyidx == -1) { break; }
-            hparams.n_intermediate = gguf_get_val_u32(ggufctx, keyidx);
-
-            keyidx = gguf_find_key(ggufctx, "bert.attention.head_count");
-            if (keyidx == -1) { break; }
-            hparams.n_head = gguf_get_val_u32(ggufctx, keyidx);
-
-            keyidx = gguf_find_key(ggufctx, "bert.block_count");
-            if (keyidx == -1) { break; }
-            hparams.n_layer = gguf_get_val_u32(ggufctx, keyidx);
-
-            ok = true;
-        } while (false);
-
-        if (!ok) {
-            fprintf(stderr, "%s: required hparam missing!\n", __func__);
-            return nullptr;
-        }
-
-#if defined(DEBUG_BERT)
-        printf("%s: n_max_tokens   = %d\n", __func__, hparams.n_max_tokens);
-        printf("%s: n_embd         = %d\n", __func__, hparams.n_embd);
-        printf("%s: n_intermediate = %d\n", __func__, hparams.n_intermediate);
-        printf("%s: n_head         = %d\n", __func__, hparams.n_head);
-        printf("%s: n_layer        = %d\n", __func__, hparams.n_layer);
-#endif
-    }
-
-    // load vocab
-    {
-        auto & hparams = model.hparams;
-
-        int keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.model");
-        if (keyidx == -1) {
-            fprintf(stderr, "%s: tokenizer model not found!\n", __func__);
-            return nullptr;
-        }
-        if (strcmp(gguf_get_val_str(ggufctx, keyidx), "bert") != 0) {
-            fprintf(stderr, "%s: tokenizer model not supported!\n", __func__);
-            return nullptr;
-        }
-
-        int tokens_keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.tokens");
-        if (tokens_keyidx == -1) {
-            fprintf(stderr, "%s: bert tokenizer vocab not found!\n", __func__);
-            return nullptr;
-        }
-
-        hparams.n_vocab = gguf_get_arr_n(ggufctx, tokens_keyidx);
-        printf("%s: bert tokenizer vocab = %d\n", __func__, int(hparams.n_vocab));
-
-        for (int i = 0; i < hparams.n_vocab; i++) {
-            std::string word = gguf_get_arr_str(ggufctx, tokens_keyidx, i);
-
-            if (word[0] == '#' && word[1] == '#')
-            {
-                vocab.subword_token_to_id[word.substr(2)] = i;
-                vocab._id_to_subword_token[i] = word;
-            }
-
-            if (vocab.token_to_id.count(word) == 0)
-            {
-                vocab.token_to_id[word] = i;
-                vocab._id_to_token[i] = word;
-            }
-        }
-    }
-
-    auto &ctx = model.ctx;
-
-#if defined(DEBUG_BERT)
-    printf("%s: ggml ctx size = %6.2f MB\n", __func__, ggml_get_mem_size(ctx) / (1024.0 * 1024.0));
-#endif
-
-    // prepare memory for the weights
-    {
-        const int n_layer = model.hparams.n_layer;
-        model.layers.resize(n_layer);
-
-        model.word_embeddings       = ggml_get_tensor(ctx, "token_embd.weight");
-        model.token_type_embeddings = ggml_get_tensor(ctx, "token_types.weight");
-        model.position_embeddings   = ggml_get_tensor(ctx, "position_embd.weight");
-        model.ln_e_w                = ggml_get_tensor(ctx, "output_norm.weight");
-        model.ln_e_b                = ggml_get_tensor(ctx, "output_norm.bias");
-
-        auto name = [](int i, std::string n) {
-            static std::string key;
-            key = "blk." + std::to_string(i) + "." + n;
-            return key.c_str();
-        };
-
-        for (int i = 0; i < n_layer; ++i)
-        {
-            auto &layer = model.layers[i];
-
-            layer.ln_att_w = ggml_get_tensor(ctx, name(i, "attn_norm.weight"));
-            layer.ln_att_b = ggml_get_tensor(ctx, name(i, "attn_norm.bias"));
-            layer.ln_out_w = ggml_get_tensor(ctx, name(i, "ffn_norm.weight"));
-            layer.ln_out_b = ggml_get_tensor(ctx, name(i, "ffn_norm.bias"));
-            layer.q_w      = ggml_get_tensor(ctx, name(i, "attn_q.weight"));
-            layer.q_b      = ggml_get_tensor(ctx, name(i, "attn_q.bias"));
-            layer.k_w      = ggml_get_tensor(ctx, name(i, "attn_k.weight"));
-            layer.k_b      = ggml_get_tensor(ctx, name(i, "attn_k.bias"));
-            layer.v_w      = ggml_get_tensor(ctx, name(i, "attn_v.weight"));
-            layer.v_b      = ggml_get_tensor(ctx, name(i, "attn_v.bias"));
-            layer.o_w      = ggml_get_tensor(ctx, name(i, "attn_output.weight"));
-            layer.o_b      = ggml_get_tensor(ctx, name(i, "attn_output.bias"));
-            layer.ff_i_w   = ggml_get_tensor(ctx, name(i, "ffn_up.weight"));
-            layer.ff_i_b   = ggml_get_tensor(ctx, name(i, "ffn_up.bias"));
-            layer.ff_o_w   = ggml_get_tensor(ctx, name(i, "ffn_down.weight"));
-            layer.ff_o_b   = ggml_get_tensor(ctx, name(i, "ffn_down.bias"));
-        }
-    }
-
-    // Calculate space requirements for setting up context buffers later
-    {
-        bert_vocab_id tokens[] = {0, 1, 2, 3};
-        // TODO: We set the initial buffer size to 16MB and hope it's enough. Maybe there is a better way to do this?
-        new_bert->buf_compute.resize(16 * 1024 * 1024);
-        bert_eval(new_bert, 1, tokens, 4, nullptr);
-        new_bert->max_batch_n = 0;
-
-        // TODO: Max tokens should be a param?
-        int32_t N = new_bert->model.hparams.n_max_tokens;
-        new_bert->mem_per_input = 2.2 * (new_bert->mem_per_token * N); // add 10% to account for ggml object overhead
-
-    }
-#if defined(DEBUG_BERT)
-    printf("%s: mem_per_token %ld KB, mem_per_input %ld MB\n", __func__, new_bert->mem_per_token / (1 << 10), new_bert->mem_per_input / (1 << 20));
-#endif
-
-    return new_bert;
-}
-
-struct BertPrivate {
-    const std::string modelPath;
-    bool modelLoaded;
-    bert_ctx *ctx = nullptr;
-    int64_t n_threads = 0;
-};
-
-Bert::Bert() : d_ptr(new BertPrivate) {
-    d_ptr->modelLoaded = false;
-}
-
-Bert::~Bert() {
-    bert_free(d_ptr->ctx);
-}
-
-bool Bert::loadModel(const std::string &modelPath)
-{
-    d_ptr->ctx = bert_load_from_file(modelPath.c_str());
-    d_ptr->n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
-    d_ptr->modelLoaded = d_ptr->ctx != nullptr;
-    fflush(stdout);
-    return true;
-}
-
-bool Bert::isModelLoaded() const
-{
-    return d_ptr->modelLoaded;
-}
-
-size_t Bert::requiredMem(const std::string &/*modelPath*/)
-{
-    return 0;
-}
-
-size_t Bert::stateSize() const
-{
-    return 0;
-}
-
-size_t Bert::saveState(uint8_t */*dest*/) const
-{
-    return 0;
-}
-
-size_t Bert::restoreState(const uint8_t */*src*/)
-{
-    return 0;
-}
-
-void Bert::setThreadCount(int32_t n_threads)
-{
-    d_ptr->n_threads = n_threads;
-}
-
-int32_t Bert::threadCount() const
-{
-    return d_ptr->n_threads;
-}
-
-std::vector<float> Bert::embedding(const std::string &text)
-{
-    const int overlap = 32;
-    const LLModel::Token clsToken = 101;
-    const size_t contextLength = bert_n_max_tokens(d_ptr->ctx);
-    typedef std::vector<LLModel::Token> TokenString;
-    TokenString tokens = ::bert_tokenize(d_ptr->ctx, text.c_str());
-#if defined(DEBUG_BERT)
-    std::cerr << "embedding: " << tokens.size()
-              << " contextLength " << contextLength
-              << "\n";
-#endif
-    std::vector<double> embeddingsSum(bert_n_embd(d_ptr->ctx), 0);
-    int embeddingsSumTotal = 0;
-    size_t start_pos = 0;
-    bool isFirstChunk = true;
-    while (start_pos < tokens.size()) {
-        TokenString chunk;
-        if (!isFirstChunk)
-            chunk.push_back(clsToken);
-        const size_t l = isFirstChunk ? contextLength : contextLength - 1;
-        if (tokens.size() - start_pos > l) {
-            chunk.insert(chunk.end(), tokens.begin() + start_pos, tokens.begin() + start_pos + l);
-            start_pos = start_pos + contextLength - overlap;
-        } else {
-            chunk.insert(chunk.end(), tokens.begin() + start_pos, tokens.end());
-            start_pos = tokens.size();
-        }
-#if defined(DEBUG_BERT)
-        std::cerr << "chunk length: " << chunk.size()
-            << " embeddingsSumTotal " << embeddingsSumTotal
-            << " contextLength " << contextLength
-            << " start_pos " << start_pos
-            << "\n";
-#endif
-        embeddingsSumTotal++;
-        std::vector<float> embeddings(bert_n_embd(d_ptr->ctx));
-        bert_eval(d_ptr->ctx, d_ptr->n_threads, chunk.data(), chunk.size(), embeddings.data());
-        std::transform(embeddingsSum.begin(), embeddingsSum.end(), embeddings.begin(), embeddingsSum.begin(), std::plus<float>());
-        isFirstChunk = false;
-    }
-
-    std::transform(embeddingsSum.begin(), embeddingsSum.end(), embeddingsSum.begin(), [embeddingsSumTotal](float num){ return num / embeddingsSumTotal; });
-    double magnitude = std::sqrt(std::inner_product(embeddingsSum.begin(), embeddingsSum.end(), embeddingsSum.begin(), 0.0));
-    for (auto &value : embeddingsSum)
-        value /= magnitude;
-    std::vector<float> finalEmbeddings(embeddingsSum.begin(), embeddingsSum.end());
-    return finalEmbeddings;
-}
-
-std::vector<LLModel::Token> Bert::tokenize(PromptContext &, const std::string &str) const
-{
-    return ::bert_tokenize(d_ptr->ctx, str.c_str());
-}
-
-LLModel::Token Bert::sampleToken(PromptContext &/*promptCtx*/) const
-{
-    return 999 /*!*/;
-}
-
-std::string Bert::tokenToString(Token id) const
-{
-    return bert_vocab_id_to_token(d_ptr->ctx, id);
-}
-
-bool Bert::evalTokens(PromptContext &ctx, const std::vector<int32_t> &tokens) const
-{
-    std::vector<float> embeddings(bert_n_embd(d_ptr->ctx));
-    int32_t cls = 101;
-    const bool useCLS = tokens.front() != cls;
-    if (useCLS) {
-        std::vector<int32_t> myTokens;
-        myTokens.push_back(cls);
-        myTokens.insert(myTokens.end(), tokens.begin(), tokens.end());
-        bert_eval(d_ptr->ctx, d_ptr->n_threads, myTokens.data(), myTokens.size(), embeddings.data());
-    } else
-        bert_eval(d_ptr->ctx, d_ptr->n_threads, tokens.data(), tokens.size(), embeddings.data());
-    ctx.n_past = 0; // bert does not store any context
-    return true;
-}
-
-int32_t Bert::contextLength() const
-{
-    return bert_n_max_tokens(d_ptr->ctx);
-}
-
-const std::vector<LLModel::Token> &Bert::endTokens() const
-{
-    static const std::vector<LLModel::Token> out = { 102 /*sep*/};
-    return out;
-}
-
-std::string get_arch_name(gguf_context *ctx_gguf) {
-    std::string arch_name;
-    const int kid = gguf_find_key(ctx_gguf, "general.architecture");
-    enum gguf_type ktype = gguf_get_kv_type(ctx_gguf, kid);
-    if (ktype != GGUF_TYPE_STRING) {
-        throw std::runtime_error("ERROR: Can't get general architecture from gguf file.");
-    }
-    return gguf_get_val_str(ctx_gguf, kid);
-}
-
-#if defined(_WIN32)
-#define DLL_EXPORT __declspec(dllexport)
-#else
-#define DLL_EXPORT __attribute__ ((visibility ("default")))
-#endif
-
-extern "C" {
-DLL_EXPORT bool is_g4a_backend_model_implementation() {
-    return true;
-}
-
-DLL_EXPORT const char *get_model_type() {
-    return modelType_;
-}
-
-DLL_EXPORT const char *get_build_variant() {
-    return GGML_BUILD_VARIANT;
-}
-
-DLL_EXPORT bool magic_match(const char * fname) {
-    struct ggml_context * ctx_meta = NULL;
-    struct gguf_init_params params = {
-        /*.no_alloc = */ true,
-        /*.ctx      = */ &ctx_meta,
-    };
-    gguf_context *ctx_gguf = gguf_init_from_file(fname, params);
-    if (!ctx_gguf)
-        return false;
-
-    bool isValid = gguf_get_version(ctx_gguf) <= 2;
-    isValid = isValid && get_arch_name(ctx_gguf) == "bert";
-
-    gguf_free(ctx_gguf);
-    return isValid;
-}
-
-DLL_EXPORT LLModel *construct() {
-    return new Bert;
-}
-}

gpt4all-backend/bert_impl.h

@@ -1,44 +0,0 @@
-#ifndef BERT_H_I_KNOW_WHAT_I_AM_DOING_WHEN_INCLUDING_THIS_FILE
-#error This file is NOT meant to be included outside of bert.cpp. Doing so is DANGEROUS. Be sure to know what you are doing before proceeding to #define BERT_H_I_KNOW_WHAT_I_AM_DOING_WHEN_INCLUDING_THIS_FILE
-#endif
-#ifndef BERT_H
-#define BERT_H
-
-#include <string>
-#include <functional>
-#include <vector>
-#include <memory>
-#include "llmodel.h"
-
-struct BertPrivate;
-class Bert : public LLModel {
-public:
-    Bert();
-    ~Bert();
-
-    bool supportsEmbedding() const override { return true; }
-    bool supportsCompletion() const override { return true; }
-    bool loadModel(const std::string &modelPath) override;
-    bool isModelLoaded() const override;
-    size_t requiredMem(const std::string &modelPath) override;
-    size_t stateSize() const override;
-    size_t saveState(uint8_t *dest) const override;
-    size_t restoreState(const uint8_t *src) override;
-    void setThreadCount(int32_t n_threads) override;
-    int32_t threadCount() const override;
-
-    std::vector<float> embedding(const std::string &text) override;
-
-private:
-    std::unique_ptr<BertPrivate> d_ptr;
-
-protected:
-    std::vector<Token> tokenize(PromptContext &, const std::string&) const override;
-    Token sampleToken(PromptContext &ctx) const override;
-    std::string tokenToString(Token) const override;
-    bool evalTokens(PromptContext &ctx, const std::vector<int32_t> &tokens) const override;
-    int32_t contextLength() const override;
-    const std::vector<Token>& endTokens() const override;
-};
-
-#endif // BERT_H

gpt4all-backend/dlhandle.h

@@ -53,6 +53,8 @@ public:
     }
 };
 #else
+#include <algorithm>
+#include <filesystem>
 #include <string>
 #include <exception>
 #include <stdexcept>
@@ -75,7 +77,9 @@ public:
 
     Dlhandle() : chandle(nullptr) {}
     Dlhandle(const std::string& fpath) {
-        chandle = LoadLibraryExA(fpath.c_str(), NULL, LOAD_LIBRARY_SEARCH_DEFAULT_DIRS | LOAD_LIBRARY_SEARCH_DLL_LOAD_DIR);
+        std::string afpath = std::filesystem::absolute(fpath).string();
+        std::replace(afpath.begin(), afpath.end(), '/', '\\');
+        chandle = LoadLibraryExA(afpath.c_str(), NULL, LOAD_LIBRARY_SEARCH_DEFAULT_DIRS | LOAD_LIBRARY_SEARCH_DLL_LOAD_DIR);
         if (!chandle) {
             throw Exception("dlopen(\""+fpath+"\"): Error");
         }

gpt4all-backend/gptj.cpp

@@ -343,7 +343,14 @@ bool gptj_eval(
     };
 
     struct ggml_context * ctx0 = ggml_init(params);
-    struct ggml_cgraph gf = {};
+    struct ggml_cgraph * gf = ggml_new_graph(ctx0);
+
+    // KQ_pos - contains the positions
+    struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
+    int * data = (int *) KQ_pos->data;
+    for (int i = 0; i < N; ++i) {
+        data[i] = n_past + i;
+    }
 
     struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
     memcpy(embd->data, embd_inp.data(), N*ggml_element_size(embd));
@@ -370,8 +377,14 @@ bool gptj_eval(
 
         // self-attention
         {
-            struct ggml_tensor * Qcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model.layers[il].c_attn_q_proj_w, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0, 0);
-            struct ggml_tensor * Kcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model.layers[il].c_attn_k_proj_w, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0, 0);
+            struct ggml_tensor * Qcur = ggml_rope(
+                ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model.layers[il].c_attn_q_proj_w, cur), n_embd/n_head, n_head, N),
+                KQ_pos, n_rot, 0, 0
+            );
+            struct ggml_tensor * Kcur = ggml_rope(
+                ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model.layers[il].c_attn_k_proj_w, cur), n_embd/n_head, n_head, N),
+                KQ_pos, n_rot, 0, 0
+            );
 
             // store key and value to memory
             {
@@ -382,8 +395,8 @@ bool gptj_eval(
                         (   n_ctx)*ggml_element_size(model.kv_self.v),
                         (il*n_ctx)*ggml_element_size(model.kv_self.v)*n_embd + n_past*ggml_element_size(model.kv_self.v));
 
-                ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Kcur, k));
-                ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Vcur, v));
+                ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k));
+                ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v));
             }
 
             // Q = Qcur.contiguous().view(n_embd/n_head, n_head, N).permute(0, 2, 1, 3)
@@ -401,11 +414,7 @@ bool gptj_eval(
             struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
 
             // KQ_scaled = KQ / sqrt(n_embd/n_head)
-            struct ggml_tensor * KQ_scaled =
-                ggml_scale(ctx0,
-                        KQ,
-                        ggml_new_f32(ctx0, 1.0f/sqrt(float(n_embd)/n_head))
-                        );
+            struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, 1.0f/sqrt(float(n_embd)/n_head));
 
             // KQ_masked = mask_past(KQ_scaled)
             struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled, n_past);
@@ -502,22 +511,22 @@ bool gptj_eval(
     // logits -> probs
     //inpL = ggml_soft_max(ctx0, inpL);
 
-    ggml_build_forward_expand(&gf, inpL);
+    ggml_build_forward_expand(gf, inpL);
 
     // run the computation
     {
         std::unique_ptr<uint8_t []> data;
-        auto plan = ggml_graph_plan(&gf, n_threads);
+        auto plan = ggml_graph_plan(gf, n_threads);
         if (plan.work_size > 0) {
             data.reset(new uint8_t[plan.work_size]);
             plan.work_data = data.get();
         }
-        ggml_graph_compute(&gf, &plan);
+        ggml_graph_compute(gf, &plan);
     }
 
     //if (n_past%100 == 0) {
-    //    ggml_graph_print   (&gf);
-    //    ggml_graph_dump_dot(&gf, NULL, "gpt-2.dot");
+    //    ggml_graph_print   (gf);
+    //    ggml_graph_dump_dot(gf, NULL, "gpt-2.dot");
     //}
 
     //embd_w.resize(n_vocab*N);
@@ -663,7 +672,9 @@ GPTJ::GPTJ()
     d_ptr->modelLoaded = false;
 }
 
-size_t GPTJ::requiredMem(const std::string &modelPath) {
+size_t GPTJ::requiredMem(const std::string &modelPath, int n_ctx, int ngl) {
+    (void)n_ctx;
+    (void)ngl;
     gptj_model dummy_model;
     gpt_vocab dummy_vocab;
     size_t mem_req;
@@ -671,19 +682,24 @@ size_t GPTJ::requiredMem(const std::string &modelPath) {
     return mem_req;
 }
 
-bool GPTJ::loadModel(const std::string &modelPath) {
+bool GPTJ::loadModel(const std::string &modelPath, int n_ctx, int ngl) {
+    (void)n_ctx;
+    (void)ngl;
+    d_ptr->modelLoaded = false;
+
     std::mt19937 rng(time(NULL));
     d_ptr->rng = rng;
 
     // load the model
-    if (!gptj_model_load(modelPath, *d_ptr->model, d_ptr->vocab)) {
+    bool ok = gptj_model_load(modelPath, *d_ptr->model, d_ptr->vocab);
+    fflush(stdout);
+    if (!ok) {
         std::cerr << "GPT-J ERROR: failed to load model from " <<  modelPath;
         return false;
     }
 
     d_ptr->n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
     d_ptr->modelLoaded = true;
-    fflush(stdout);
     return true;
 }
 
@@ -721,8 +737,10 @@ size_t GPTJ::restoreState(const uint8_t *src)
     return gptj_set_state_data(d_ptr->model, &d_ptr->rng, src);
 }
 
-std::vector<LLModel::Token> GPTJ::tokenize(PromptContext &, const std::string &str) const
+std::vector<LLModel::Token> GPTJ::tokenize(PromptContext &ctx, const std::string &str, bool special) const
 {
+    (void)ctx;
+    (void)special;
     return ::gpt_tokenize(d_ptr->vocab, str);
 }
 
@@ -806,7 +824,7 @@ DLL_EXPORT bool magic_match(const char * fname) {
     if (!ctx_gguf)
         return false;
 
-    bool isValid = gguf_get_version(ctx_gguf) <= 2;
+    bool isValid = gguf_get_version(ctx_gguf) <= 3;
     isValid = isValid && get_arch_name(ctx_gguf) == "gptj";
 
     gguf_free(ctx_gguf);

gpt4all-backend/gptj_impl.h

@@ -17,9 +17,9 @@ public:
 
     bool supportsEmbedding() const override { return false; }
     bool supportsCompletion() const override { return true; }
-    bool loadModel(const std::string &modelPath) override;
+    bool loadModel(const std::string &modelPath, int n_ctx, int ngl) override;
     bool isModelLoaded() const override;
-    size_t requiredMem(const std::string &modelPath) override;
+    size_t requiredMem(const std::string &modelPath, int n_ctx, int ngl) override;
     size_t stateSize() const override;
     size_t saveState(uint8_t *dest) const override;
     size_t restoreState(const uint8_t *src) override;
@@ -30,12 +30,13 @@ private:
     GPTJPrivate *d_ptr;
 
 protected:
-    std::vector<Token> tokenize(PromptContext &, const std::string&) const override;
+    std::vector<Token> tokenize(PromptContext &ctx, const std::string &str, bool special) const override;
     Token sampleToken(PromptContext &ctx) const override;
-    std::string tokenToString(Token) const override;
+    std::string tokenToString(Token id) const override;
     bool evalTokens(PromptContext &ctx, const std::vector<int32_t> &tokens) const override;
     int32_t contextLength() const override;
-    const std::vector<Token>& endTokens() const override;
+    const std::vector<Token> &endTokens() const override;
+    bool shouldAddBOS() const override { return false; }
 };
 
 #endif // GPTJ_H

gpt4all-backend/llama.cpp.cmake

@@ -38,6 +38,12 @@ else()
     endif()
 endif()
 
+if (APPLE)
+    set(LLAMA_KOMPUTE_DEFAULT OFF)
+else()
+    set(LLAMA_KOMPUTE_DEFAULT ON)
+endif()
+
 
 #
 # Option list
@@ -77,7 +83,7 @@ option(LLAMA_OPENBLAS               "llama: use OpenBLAS"
 #option(LLAMA_CUBLAS                 "llama: use cuBLAS"                                     OFF)
 #option(LLAMA_CLBLAST                "llama: use CLBlast"                                    OFF)
 #option(LLAMA_METAL                  "llama: use Metal"                                      OFF)
-#option(LLAMA_K_QUANTS               "llama: use k-quants"                                   ON)
+option(LLAMA_KOMPUTE                "llama: use Kompute"                                    ${LLAMA_KOMPUTE_DEFAULT})
 set(LLAMA_BLAS_VENDOR "Generic" CACHE STRING "llama: BLAS library vendor")
 set(LLAMA_CUDA_DMMV_X "32" CACHE STRING "llama: x stride for dmmv CUDA kernels")
 set(LLAMA_CUDA_DMMV_Y "1" CACHE STRING  "llama: y block size for dmmv CUDA kernels")
@@ -154,6 +160,12 @@ if (LLAMA_OPENBLAS)
 endif()
 
 if (LLAMA_KOMPUTE)
+    set(LLAMA_DIR ${CMAKE_CURRENT_SOURCE_DIR}/llama.cpp-mainline)
+    if (NOT EXISTS "${LLAMA_DIR}/kompute/CMakeLists.txt")
+        message(FATAL_ERROR "Kompute not found")
+    endif()
+    message(STATUS "Kompute found")
+
     add_compile_definitions(VULKAN_HPP_DISPATCH_LOADER_DYNAMIC=1)
     find_package(Vulkan COMPONENTS glslc REQUIRED)
     find_program(glslc_executable NAMES glslc HINTS Vulkan::glslc)
@@ -161,8 +173,6 @@ if (LLAMA_KOMPUTE)
         message(FATAL_ERROR "glslc not found")
     endif()
 
-    set(LLAMA_DIR ${CMAKE_CURRENT_SOURCE_DIR}/llama.cpp-mainline)
-
     function(compile_shader)
       set(options)
       set(oneValueArgs)
@@ -174,9 +184,10 @@ if (LLAMA_KOMPUTE)
         add_custom_command(
             OUTPUT ${spv_file}
             DEPENDS ${LLAMA_DIR}/${source}
-              ${LLAMA_DIR}/kompute/common.comp
-              ${LLAMA_DIR}/kompute/op_getrows.comp
-              ${LLAMA_DIR}/kompute/op_mul_mv_q_n.comp
+              ${LLAMA_DIR}/kompute-shaders/common.comp
+              ${LLAMA_DIR}/kompute-shaders/op_getrows.comp
+              ${LLAMA_DIR}/kompute-shaders/op_mul_mv_q_n_pre.comp
+              ${LLAMA_DIR}/kompute-shaders/op_mul_mv_q_n.comp
             COMMAND ${glslc_executable} --target-env=vulkan1.2 -o ${spv_file} ${LLAMA_DIR}/${source}
             COMMENT "Compiling ${source} to ${source}.spv"
         )
@@ -196,7 +207,7 @@ if (LLAMA_KOMPUTE)
               COMMAND ${CMAKE_COMMAND} -E echo \"\#define ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE}
               COMMAND ${CMAKE_COMMAND} -E echo "namespace kp {" >> ${OUTPUT_HEADER_FILE}
               COMMAND ${CMAKE_COMMAND} -E echo "namespace shader_data {" >> ${OUTPUT_HEADER_FILE}
-              COMMAND ${CMAKE_BINARY_DIR}/bin/$<CONFIG>/xxd -i ${spv_file} >> ${OUTPUT_HEADER_FILE}
+              COMMAND ${CMAKE_BINARY_DIR}/bin/$<CONFIG>/xxd -i ${RAW_FILE_NAME} >> ${OUTPUT_HEADER_FILE}
               COMMAND ${CMAKE_COMMAND} -E echo "}}" >> ${OUTPUT_HEADER_FILE}
               COMMAND ${CMAKE_COMMAND} -E echo \"\#endif // define ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE}
               DEPENDS ${spv_file} xxd
@@ -210,7 +221,7 @@ if (LLAMA_KOMPUTE)
               COMMAND ${CMAKE_COMMAND} -E echo \"\#define ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE}
               COMMAND ${CMAKE_COMMAND} -E echo "namespace kp {" >> ${OUTPUT_HEADER_FILE}
               COMMAND ${CMAKE_COMMAND} -E echo "namespace shader_data {" >> ${OUTPUT_HEADER_FILE}
-              COMMAND ${CMAKE_BINARY_DIR}/bin/xxd -i ${spv_file} >> ${OUTPUT_HEADER_FILE}
+              COMMAND ${CMAKE_BINARY_DIR}/bin/xxd -i ${RAW_FILE_NAME} >> ${OUTPUT_HEADER_FILE}
               COMMAND ${CMAKE_COMMAND} -E echo "}}" >> ${OUTPUT_HEADER_FILE}
               COMMAND ${CMAKE_COMMAND} -E echo \"\#endif // define ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE}
               DEPENDS ${spv_file} xxd
@@ -220,89 +231,86 @@ if (LLAMA_KOMPUTE)
       endforeach()
     endfunction()
 
-    if (EXISTS "${LLAMA_DIR}/kompute/CMakeLists.txt")
-        message(STATUS "Kompute found")
-        set(KOMPUTE_OPT_LOG_LEVEL Critical CACHE STRING "Kompute log level")
-        add_subdirectory(${LLAMA_DIR}/kompute)
+    set(KOMPUTE_OPT_LOG_LEVEL Critical CACHE STRING "Kompute log level")
+    add_subdirectory(${LLAMA_DIR}/kompute)
 
-        # Compile our shaders
-        compile_shader(SOURCES
-          kompute/op_scale.comp
-          kompute/op_add.comp
-          kompute/op_addrow.comp
-          kompute/op_mul.comp
-          kompute/op_mulrow.comp
-          kompute/op_silu.comp
-          kompute/op_relu.comp
-          kompute/op_gelu.comp
-          kompute/op_softmax.comp
-          kompute/op_norm.comp
-          kompute/op_rmsnorm.comp
-          kompute/op_diagmask.comp
-          kompute/op_mul_mat_mat_f32.comp
-          kompute/op_mul_mat_f16.comp
-          kompute/op_mul_mat_q8_0.comp
-          kompute/op_mul_mat_q4_0.comp
-          kompute/op_mul_mat_q4_1.comp
-          kompute/op_mul_mat_q6_k.comp
-          kompute/op_getrows_f16.comp
-          kompute/op_getrows_q4_0.comp
-          kompute/op_getrows_q4_1.comp
-          kompute/op_getrows_q6_k.comp
-          kompute/op_rope.comp
-          kompute/op_cpy_f16_f16.comp
-          kompute/op_cpy_f16_f32.comp
-          kompute/op_cpy_f32_f16.comp
-          kompute/op_cpy_f32_f32.comp
-        )
+    # Compile our shaders
+    compile_shader(SOURCES
+      kompute-shaders/op_scale.comp
+      kompute-shaders/op_scale_8.comp
+      kompute-shaders/op_add.comp
+      kompute-shaders/op_addrow.comp
+      kompute-shaders/op_mul.comp
+      kompute-shaders/op_silu.comp
+      kompute-shaders/op_relu.comp
+      kompute-shaders/op_gelu.comp
+      kompute-shaders/op_softmax.comp
+      kompute-shaders/op_norm.comp
+      kompute-shaders/op_rmsnorm.comp
+      kompute-shaders/op_diagmask.comp
+      kompute-shaders/op_mul_mat_mat_f32.comp
+      kompute-shaders/op_mul_mat_f16.comp
+      kompute-shaders/op_mul_mat_q8_0.comp
+      kompute-shaders/op_mul_mat_q4_0.comp
+      kompute-shaders/op_mul_mat_q4_1.comp
+      kompute-shaders/op_mul_mat_q6_k.comp
+      kompute-shaders/op_getrows_f16.comp
+      kompute-shaders/op_getrows_q4_0.comp
+      kompute-shaders/op_getrows_q4_1.comp
+      kompute-shaders/op_getrows_q6_k.comp
+      kompute-shaders/op_rope_f16.comp
+      kompute-shaders/op_rope_f32.comp
+      kompute-shaders/op_cpy_f16_f16.comp
+      kompute-shaders/op_cpy_f16_f32.comp
+      kompute-shaders/op_cpy_f32_f16.comp
+      kompute-shaders/op_cpy_f32_f32.comp
+    )
 
-        # Create a custom target for our generated shaders
-        add_custom_target(generated_shaders DEPENDS
-          shaderop_scale.h
-          shaderop_add.h
-          shaderop_addrow.h
-          shaderop_mul.h
-          shaderop_mulrow.h
-          shaderop_silu.h
-          shaderop_relu.h
-          shaderop_gelu.h
-          shaderop_softmax.h
-          shaderop_norm.h
-          shaderop_rmsnorm.h
-          shaderop_diagmask.h
-          shaderop_mul_mat_mat_f32.h
-          shaderop_mul_mat_f16.h
-          shaderop_mul_mat_q8_0.h
-          shaderop_mul_mat_q4_0.h
-          shaderop_mul_mat_q4_1.h
-          shaderop_mul_mat_q6_k.h
-          shaderop_getrows_f16.h
-          shaderop_getrows_q4_0.h
-          shaderop_getrows_q4_1.h
-          shaderop_getrows_q6_k.h
-          shaderop_rope.h
-          shaderop_cpy_f16_f16.h
-          shaderop_cpy_f16_f32.h
-          shaderop_cpy_f32_f16.h
-          shaderop_cpy_f32_f32.h
-        )
+    # Create a custom target for our generated shaders
+    add_custom_target(generated_shaders DEPENDS
+      shaderop_scale.h
+      shaderop_scale_8.h
+      shaderop_add.h
+      shaderop_addrow.h
+      shaderop_mul.h
+      shaderop_silu.h
+      shaderop_relu.h
+      shaderop_gelu.h
+      shaderop_softmax.h
+      shaderop_norm.h
+      shaderop_rmsnorm.h
+      shaderop_diagmask.h
+      shaderop_mul_mat_mat_f32.h
+      shaderop_mul_mat_f16.h
+      shaderop_mul_mat_q8_0.h
+      shaderop_mul_mat_q4_0.h
+      shaderop_mul_mat_q4_1.h
+      shaderop_mul_mat_q6_k.h
+      shaderop_getrows_f16.h
+      shaderop_getrows_q4_0.h
+      shaderop_getrows_q4_1.h
+      shaderop_getrows_q6_k.h
+      shaderop_rope_f16.h
+      shaderop_rope_f32.h
+      shaderop_cpy_f16_f16.h
+      shaderop_cpy_f16_f32.h
+      shaderop_cpy_f32_f16.h
+      shaderop_cpy_f32_f32.h
+    )
 
-        # Create a custom command that depends on the generated_shaders
-        add_custom_command(
-            OUTPUT ${CMAKE_CURRENT_BINARY_DIR}/ggml-vulkan.stamp
-            COMMAND ${CMAKE_COMMAND} -E touch ${CMAKE_CURRENT_BINARY_DIR}/ggml-vulkan.stamp
-            DEPENDS generated_shaders
-            COMMENT "Ensuring shaders are generated before compiling ggml-vulkan.cpp"
-        )
+    # Create a custom command that depends on the generated_shaders
+    add_custom_command(
+        OUTPUT ${CMAKE_CURRENT_BINARY_DIR}/ggml-kompute.stamp
+        COMMAND ${CMAKE_COMMAND} -E touch ${CMAKE_CURRENT_BINARY_DIR}/ggml-kompute.stamp
+        DEPENDS generated_shaders
+        COMMENT "Ensuring shaders are generated before compiling ggml-kompute.cpp"
+    )
 
-        # Add the stamp to the main sources to ensure dependency tracking
-        set(GGML_SOURCES_KOMPUTE ${LLAMA_DIR}/ggml-vulkan.cpp ${LLAMA_DIR}/ggml-vulkan.h ${CMAKE_CURRENT_BINARY_DIR}/ggml-vulkan.stamp)
-        add_compile_definitions(GGML_USE_KOMPUTE)
-        set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} kompute)
-        set(LLAMA_EXTRA_INCLUDES ${LLAMA_EXTRA_INCLUDES} ${CMAKE_BINARY_DIR})
-    else()
-        message(WARNING "Kompute not found")
-    endif()
+    # Add the stamp to the main sources to ensure dependency tracking
+    set(GGML_SOURCES_KOMPUTE ${LLAMA_DIR}/ggml-kompute.cpp ${LLAMA_DIR}/ggml-kompute.h ${CMAKE_CURRENT_BINARY_DIR}/ggml-kompute.stamp)
+    add_compile_definitions(GGML_USE_KOMPUTE)
+    set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} kompute)
+    set(LLAMA_EXTRA_INCLUDES ${LLAMA_EXTRA_INCLUDES} ${CMAKE_BINARY_DIR})
 endif()
 
 if (LLAMA_ALL_WARNINGS)
@@ -564,33 +572,26 @@ function(include_ggml DIRECTORY SUFFIX WITH_LLAMA)
         endif()
     endif()
 
-    set(GGML_SOURCES_QUANT_K )
-    set(GGML_METAL_SOURCES )
-    if (LLAMA_K_QUANTS)
-        set(GGML_SOURCES_QUANT_K
-            ${DIRECTORY}/k_quants.h
-            ${DIRECTORY}/k_quants.c)
+    set(GGML_METAL_SOURCES)
+    if (LLAMA_METAL)
+        find_library(FOUNDATION_LIBRARY         Foundation              REQUIRED)
+        find_library(METAL_FRAMEWORK            Metal                   REQUIRED)
+        find_library(METALKIT_FRAMEWORK         MetalKit                REQUIRED)
+        find_library(METALPERFORMANCE_FRAMEWORK MetalPerformanceShaders REQUIRED)
 
-        if (LLAMA_METAL)
-            find_library(FOUNDATION_LIBRARY         Foundation              REQUIRED)
-            find_library(METAL_FRAMEWORK            Metal                   REQUIRED)
-            find_library(METALKIT_FRAMEWORK         MetalKit                REQUIRED)
-            find_library(METALPERFORMANCE_FRAMEWORK MetalPerformanceShaders REQUIRED)
+        set(GGML_METAL_SOURCES ${DIRECTORY}/ggml-metal.m ${DIRECTORY}/ggml-metal.h)
+        # get full path to the file
+        #add_compile_definitions(GGML_METAL_DIR_KERNELS="${CMAKE_CURRENT_SOURCE_DIR}/")
 
-            set(GGML_METAL_SOURCES ${DIRECTORY}/ggml-metal.m ${DIRECTORY}/ggml-metal.h)
-            # get full path to the file
-            #add_compile_definitions(GGML_METAL_DIR_KERNELS="${CMAKE_CURRENT_SOURCE_DIR}/")
+        # copy ggml-metal.metal to bin directory
+        configure_file(${DIRECTORY}/ggml-metal.metal bin/ggml-metal.metal COPYONLY)
 
-            # copy ggml-metal.metal to bin directory
-            configure_file(${DIRECTORY}/ggml-metal.metal bin/ggml-metal.metal COPYONLY)
-
-            set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS}
-                ${FOUNDATION_LIBRARY}
-                ${METAL_FRAMEWORK}
-                ${METALKIT_FRAMEWORK}
-                ${METALPERFORMANCE_FRAMEWORK}
-            )
-        endif()
+        set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS}
+            ${FOUNDATION_LIBRARY}
+            ${METAL_FRAMEWORK}
+            ${METALKIT_FRAMEWORK}
+            ${METALPERFORMANCE_FRAMEWORK}
+        )
     endif()
 
     add_library(ggml${SUFFIX} OBJECT
@@ -598,16 +599,15 @@ function(include_ggml DIRECTORY SUFFIX WITH_LLAMA)
                 ${DIRECTORY}/ggml.h
                 ${DIRECTORY}/ggml-alloc.c
                 ${DIRECTORY}/ggml-alloc.h
-                ${GGML_SOURCES_QUANT_K}
+                ${DIRECTORY}/ggml-backend.c
+                ${DIRECTORY}/ggml-backend.h
+                ${DIRECTORY}/ggml-quants.h
+                ${DIRECTORY}/ggml-quants.c
                 ${GGML_SOURCES_CUDA}
                 ${GGML_METAL_SOURCES}
                 ${GGML_OPENCL_SOURCES}
                 ${GGML_SOURCES_KOMPUTE})
 
-    if (LLAMA_K_QUANTS)
-        target_compile_definitions(ggml${SUFFIX} PUBLIC GGML_USE_K_QUANTS)
-    endif()
-
     if (LLAMA_METAL AND GGML_METAL_SOURCES)
         target_compile_definitions(ggml${SUFFIX} PUBLIC GGML_USE_METAL GGML_METAL_NDEBUG)
     endif()

gpt4all-backend/llamamodel.cpp

@@ -6,34 +6,43 @@
 #include <cstdio>
 #include <cstring>
 #include <fstream>
-#include <map>
-#include <string>
-#include <vector>
+#include <initializer_list>
+#include <iomanip>
 #include <iostream>
-#if defined(_WIN32) && defined(_MSC_VER)
-    #define WIN32_LEAN_AND_MEAN
-    #ifndef NOMINMAX
-        #define NOMINMAX
-    #endif
-    #include <windows.h>
-    #include <io.h>
-    #include <stdio.h>
-#else
-    #include <unistd.h>
-#endif
+#include <map>
+#include <numeric>
 #include <random>
+#include <sstream>
+#include <stdexcept>
+#include <string>
 #include <thread>
 #include <unordered_set>
+#include <vector>
 
 #include <llama.h>
 #include <ggml.h>
-
 #ifdef GGML_USE_KOMPUTE
-#include "ggml-vulkan.h"
+#include <ggml-kompute.h>
 #endif
 
-namespace {
-const char *modelType_ = "LLaMA";
+using namespace std::string_literals;
+
+// Maximum supported GGUF version
+static constexpr int GGUF_VER_MAX = 3;
+
+static const char * const modelType_ = "LLaMA";
+
+static const std::vector<const char *> KNOWN_ARCHES {
+    "baichuan", "bert", "bloom", "codeshell", "falcon", "gemma", "gpt2", "llama", "mpt", "nomic-bert", "orion",
+    "persimmon", "phi2", "plamo", "qwen", "qwen2", "refact", "stablelm", "starcoder"
+};
+
+static const std::vector<const char *> EMBEDDING_ARCHES {
+    "bert", "nomic-bert"
+};
+
+static bool is_embedding_arch(const std::string &arch) {
+    return std::find(EMBEDDING_ARCHES.begin(), EMBEDDING_ARCHES.end(), arch) < EMBEDDING_ARCHES.end();
 }
 
 static bool llama_verbose() {
@@ -58,7 +67,7 @@ struct gpt_params {
 
     std::string prompt = "";
 
-    bool memory_f16        = true;  // use f16 instead of f32 for memory kv
+    enum ggml_type kv_type = GGML_TYPE_F16; // use f16 instead of f32 for memory kv
 
     bool use_mmap          = true;  // use mmap for faster loads
     bool use_mlock         = false; // use mlock to keep model in memory
@@ -70,10 +79,12 @@ static int llama_sample_top_p_top_k(
         int last_n_tokens_size,
         int top_k,
         float top_p,
+        float min_p,
         float temp,
-        float repeat_penalty) {
-    auto logits = llama_get_logits(ctx);
-    auto n_vocab = llama_n_vocab(ctx);
+        float repeat_penalty,
+        int32_t pos) {
+    auto logits = llama_get_logits_ith(ctx, pos);
+    auto n_vocab = llama_n_vocab(llama_get_model(ctx));
     // Populate initial list of all candidates
     std::vector<llama_token_data> candidates;
     candidates.reserve(n_vocab);
@@ -82,21 +93,77 @@ static int llama_sample_top_p_top_k(
     }
     llama_token_data_array candidates_p = {candidates.data(), candidates.size(), false};
     // Sample repeat penalty
-    llama_sample_repetition_penalty(nullptr, &candidates_p, last_n_tokens_data, last_n_tokens_size, repeat_penalty);
+    llama_sample_repetition_penalties(nullptr, &candidates_p, last_n_tokens_data, last_n_tokens_size, repeat_penalty, 0.0f, 0.0f);
     // Temperature sampling
     llama_sample_top_k(ctx, &candidates_p, top_k, 1);
     llama_sample_tail_free(ctx, &candidates_p, 1.0f, 1);
     llama_sample_typical(ctx, &candidates_p, 1.0f, 1);
     llama_sample_top_p(ctx, &candidates_p, top_p, 1);
-    llama_sample_temperature(ctx, &candidates_p, temp);
+    llama_sample_min_p(ctx, &candidates_p, min_p, 1);
+    llama_sample_temp(ctx, &candidates_p, temp);
     return llama_sample_token(ctx, &candidates_p);
 }
 
+std::string get_arch_name(gguf_context *ctx_gguf) {
+    std::string arch_name;
+    const int kid = gguf_find_key(ctx_gguf, "general.architecture");
+    enum gguf_type ktype = gguf_get_kv_type(ctx_gguf, kid);
+    if (ktype != (GGUF_TYPE_STRING)) {
+        throw std::runtime_error("ERROR: Can't get general architecture from gguf file.");
+    }
+    return gguf_get_val_str(ctx_gguf, kid);
+}
+
+static gguf_context *load_gguf(const char *fname) {
+    struct gguf_init_params params = {
+        /*.no_alloc = */ true,
+        /*.ctx      = */ nullptr,
+    };
+    gguf_context *ctx = gguf_init_from_file(fname, params);
+    if (!ctx) {
+        std::cerr << __func__ << ": gguf_init_from_file failed\n";
+        return nullptr;
+    }
+
+    int gguf_ver = gguf_get_version(ctx);
+    if (gguf_ver > GGUF_VER_MAX) {
+        std::cerr << __func__ << ": unsupported gguf version: " << gguf_ver << "\n";
+        gguf_free(ctx);
+        return nullptr;
+    }
+
+    return ctx;
+}
+
+static int32_t get_arch_key_u32(std::string const &modelPath, std::string const &archKey) {
+    auto * ctx = load_gguf(modelPath.c_str());
+    if (!ctx)
+        return -1;
+    std::string arch = get_arch_name(ctx);
+
+    int32_t value = -1;
+    if (ctx) {
+        auto key = arch + "." + archKey;
+        int keyidx = gguf_find_key(ctx, key.c_str());
+        if (keyidx != -1) {
+            value = gguf_get_val_u32(ctx, keyidx);
+        } else {
+            std::cerr << __func__ << ": " << key << "not found in " << modelPath << "\n";
+        }
+    }
+
+    gguf_free(ctx);
+    return value;
+}
+
 struct LLamaPrivate {
     const std::string modelPath;
     bool modelLoaded;
+    int device = -1;
+    llama_model *model = nullptr;
     llama_context *ctx = nullptr;
-    llama_context_params params;
+    llama_model_params model_params;
+    llama_context_params ctx_params;
     int64_t n_threads = 0;
     std::vector<LLModel::Token> end_tokens;
 };
@@ -117,7 +184,9 @@ struct llama_file_hparams {
     enum llama_ftype ftype = LLAMA_FTYPE_MOSTLY_F16;
 };
 
-size_t LLamaModel::requiredMem(const std::string &modelPath) {
+size_t LLamaModel::requiredMem(const std::string &modelPath, int n_ctx, int ngl) {
+    // TODO(cebtenzzre): update to GGUF
+    (void)ngl; // FIXME(cetenzzre): use this value
     auto fin = std::ifstream(modelPath, std::ios::binary);
     fin.seekg(0, std::ios_base::end);
     size_t filesize = fin.tellg();
@@ -134,69 +203,181 @@ size_t LLamaModel::requiredMem(const std::string &modelPath) {
     fin.read(reinterpret_cast<char*>(&hparams.n_layer), sizeof(hparams.n_layer));
     fin.read(reinterpret_cast<char*>(&hparams.n_rot), sizeof(hparams.n_rot));
     fin.read(reinterpret_cast<char*>(&hparams.ftype), sizeof(hparams.ftype));
-    const size_t n_ctx = 2048;
     const size_t kvcache_element_size = 2; // fp16
     const size_t est_kvcache_size = hparams.n_embd * hparams.n_layer * 2u * n_ctx * kvcache_element_size;
     return filesize + est_kvcache_size;
 }
 
-bool LLamaModel::loadModel(const std::string &modelPath)
-{
-    // load the model
-    d_ptr->params = llama_context_default_params();
-
-    gpt_params params;
-    d_ptr->params.n_ctx      = 2048;
-    d_ptr->params.seed       = params.seed;
-    d_ptr->params.f16_kv     = params.memory_f16;
-    d_ptr->params.use_mmap   = params.use_mmap;
-#if defined (__APPLE__)
-    d_ptr->params.use_mlock  = true;
-#else
-    d_ptr->params.use_mlock  = params.use_mlock;
-#endif
-#ifdef GGML_USE_METAL
-    if (llama_verbose()) {
-        std::cerr << "llama.cpp: using Metal" << std::endl;
-    }
-    // metal always runs the whole model if n_gpu_layers is not 0, at least
-    // currently
-    d_ptr->params.n_gpu_layers = 1;
-#endif
-#ifdef GGML_USE_KOMPUTE
-    if (ggml_vk_has_device()) {
-        // vulkan always runs the whole model if n_gpu_layers is not 0, at least
-        // currently
-        d_ptr->params.n_gpu_layers = 1;
-    }
-#endif
-
-    d_ptr->ctx = llama_init_from_file(modelPath.c_str(), d_ptr->params);
-    if (!d_ptr->ctx) {
-#ifdef GGML_USE_KOMPUTE
-        // Explicitly free the device so next load it doesn't use it
-        ggml_vk_free_device();
-#endif
-        std::cerr << "LLAMA ERROR: failed to load model from " <<  modelPath << std::endl;
+bool LLamaModel::isModelBlacklisted(const std::string &modelPath) const {
+    auto * ctx = load_gguf(modelPath.c_str());
+    if (!ctx) {
+        std::cerr << __func__ << ": failed to load " << modelPath << "\n";
         return false;
     }
 
-    d_ptr->end_tokens = {llama_token_eos(d_ptr->ctx)};
+    auto get_key = [ctx, &modelPath](const char *name) {
+        int keyidx = gguf_find_key(ctx, name);
+        if (keyidx == -1) {
+            throw std::logic_error(name + " not found in "s + modelPath);
+        }
+        return keyidx;
+    };
+
+    bool res = false;
+    try {
+        std::string name(gguf_get_val_str(ctx, get_key("general.name")));
+        int token_idx = get_key("tokenizer.ggml.tokens");
+        int n_vocab = gguf_get_arr_n(ctx, token_idx);
+
+        // check for known bad models
+        if (name == "open-orca_mistral-7b-openorca"
+            && n_vocab == 32002
+            && gguf_get_arr_str(ctx, token_idx, 32000) == "<dummy32000>"s // should be <|im_end|>
+        ) {
+            res = true;
+        }
+    } catch (const std::logic_error &e) {
+        std::cerr << __func__ << ": " << e.what() << "\n";
+    }
+
+    gguf_free(ctx);
+    return res;
+}
+
+bool LLamaModel::isEmbeddingModel(const std::string &modelPath) const {
+    auto *ctx_gguf = load_gguf(modelPath.c_str());
+    if (!ctx_gguf) {
+        std::cerr << __func__ << ": failed to load GGUF from " <<  modelPath << "\n";
+        return false;
+    }
+
+    std::string arch = get_arch_name(ctx_gguf);
+    gguf_free(ctx_gguf);
+    return is_embedding_arch(arch);
+}
+
+bool LLamaModel::loadModel(const std::string &modelPath, int n_ctx, int ngl)
+{
+    d_ptr->modelLoaded = false;
+
+    // clean up after previous loadModel()
+    if (d_ptr->model) {
+        llama_free_model(d_ptr->model);
+        d_ptr->model = nullptr;
+    }
+    if (d_ptr->ctx) {
+        llama_free(d_ptr->ctx);
+        d_ptr->ctx = nullptr;
+    }
+
+    if (n_ctx < 8) {
+        std::cerr << "warning: minimum context size is 8, using minimum size.\n";
+        n_ctx = 8;
+    }
+
+    // -- load the model --
+
+    gpt_params params;
+
+    d_ptr->model_params = llama_model_default_params();
+
+    d_ptr->model_params.use_mmap  = params.use_mmap;
+#if defined (__APPLE__)
+    d_ptr->model_params.use_mlock = true;
+#else
+    d_ptr->model_params.use_mlock = params.use_mlock;
+#endif
+
+    d_ptr->model_params.progress_callback = &LLModel::staticProgressCallback;
+    d_ptr->model_params.progress_callback_user_data = this;
 
 #ifdef GGML_USE_KOMPUTE
-    if (ggml_vk_has_device()) {
+    if (d_ptr->device != -1) {
+        d_ptr->model_params.main_gpu = d_ptr->device;
+        d_ptr->model_params.n_gpu_layers = ngl;
+    }
+#elif defined(GGML_USE_METAL)
+    (void)ngl;
+
+    if (llama_verbose()) {
+        std::cerr << "llama.cpp: using Metal" << std::endl;
+    }
+
+    // always fully offload on Metal
+    // TODO(cebtenzzre): use this parameter to allow using more than 53% of system RAM to load a model
+    d_ptr->model_params.n_gpu_layers = 100;
+#else
+    (void)ngl;
+#endif
+
+    d_ptr->model = llama_load_model_from_file_gpt4all(modelPath.c_str(), &d_ptr->model_params);
+    if (!d_ptr->model) {
+        fflush(stdout);
+        d_ptr->device = -1;
+        std::cerr << "LLAMA ERROR: failed to load model from " << modelPath << std::endl;
+        return false;
+    }
+
+    // -- initialize the context --
+
+    d_ptr->ctx_params = llama_context_default_params();
+
+    bool isEmbedding = is_embedding_arch(llama_model_arch(d_ptr->model));
+    const int n_ctx_train = llama_n_ctx_train(d_ptr->model);
+    if (isEmbedding) {
+        d_ptr->ctx_params.n_batch = n_ctx_train;
+    } else {
+        if (n_ctx > n_ctx_train) {
+            std::cerr << "warning: model was trained on only " << n_ctx_train << " context tokens ("
+                      << n_ctx << " specified)\n";
+        }
+    }
+
+    d_ptr->ctx_params.n_ctx   = n_ctx;
+    d_ptr->ctx_params.seed    = params.seed;
+    d_ptr->ctx_params.type_k  = params.kv_type;
+    d_ptr->ctx_params.type_v  = params.kv_type;
+
+    // The new batch API provides space for n_vocab*n_tokens logits. Tell llama.cpp early
+    // that we want this many logits so the state serializes consistently.
+    d_ptr->ctx_params.logits_all = true;
+
+    d_ptr->n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
+    d_ptr->ctx_params.n_threads       = d_ptr->n_threads;
+    d_ptr->ctx_params.n_threads_batch = d_ptr->n_threads;
+
+    if (isEmbedding)
+        d_ptr->ctx_params.embeddings = true;
+
+    d_ptr->ctx = llama_new_context_with_model(d_ptr->model, d_ptr->ctx_params);
+    if (!d_ptr->ctx) {
+        fflush(stdout);
+        std::cerr << "LLAMA ERROR: failed to init context for model " <<  modelPath << std::endl;
+        llama_free_model(d_ptr->model);
+        d_ptr->model = nullptr;
+        d_ptr->device = -1;
+        return false;
+    }
+
+    d_ptr->end_tokens = {llama_token_eos(d_ptr->model)};
+
+#ifdef GGML_USE_KOMPUTE
+    if (usingGPUDevice() && ggml_vk_has_device()) {
         std::cerr << "llama.cpp: using Vulkan on " << ggml_vk_current_device().name << std::endl;
     }
 #endif
 
-    d_ptr->n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
+    m_supportsEmbedding = isEmbedding;
+    m_supportsCompletion = !isEmbedding;
+
+    fflush(stdout);
     d_ptr->modelLoaded = true;
-    fflush(stderr);
     return true;
 }
 
 void LLamaModel::setThreadCount(int32_t n_threads) {
     d_ptr->n_threads = n_threads;
+    llama_set_n_threads(d_ptr->ctx, n_threads, n_threads);
 }
 
 int32_t LLamaModel::threadCount() const {
@@ -208,6 +389,7 @@ LLamaModel::~LLamaModel()
     if (d_ptr->ctx) {
         llama_free(d_ptr->ctx);
     }
+    llama_free_model(d_ptr->model);
 }
 
 bool LLamaModel::isModelLoaded() const
@@ -231,18 +413,20 @@ size_t LLamaModel::restoreState(const uint8_t *src)
     return llama_set_state_data(d_ptr->ctx, const_cast<uint8_t*>(src));
 }
 
-std::vector<LLModel::Token> LLamaModel::tokenize(PromptContext &ctx, const std::string &str) const
+std::vector<LLModel::Token> LLamaModel::tokenize(PromptContext &ctx, const std::string &str, bool special) const
 {
-    const bool useBOS = ctx.n_past == 0 && (ctx.tokens.empty() || ctx.tokens.front() != llama_token_bos(d_ptr->ctx));
-    std::vector<LLModel::Token> fres(str.size()+4);
-    auto fres_len = llama_tokenize(d_ptr->ctx, str.c_str(), str.length(), fres.data(), fres.size(), useBOS);
+    const bool wantBOS = ctx.n_past == 0 && ctx.tokens.empty();
+    const bool useBOS = wantBOS && shouldAddBOS();
+    auto strCat = wantBOS && !special ? " " + str : str; // insert leading space ourselves, llama.cpp fork doesn't anymore
+    std::vector<LLModel::Token> fres(strCat.size()+4);
+    auto fres_len = llama_tokenize(d_ptr->model, strCat.c_str(), strCat.length(), fres.data(), fres.size(), useBOS, special);
     fres.resize(fres_len);
     return fres;
 }
 
 std::string LLamaModel::tokenToString(Token id) const
 {
-    return llama_token_to_str(d_ptr->ctx, id);
+    return llama_token_to_piece(d_ptr->ctx, id);
 }
 
 LLModel::Token LLamaModel::sampleToken(PromptContext &promptCtx) const
@@ -250,13 +434,33 @@ LLModel::Token LLamaModel::sampleToken(PromptContext &promptCtx) const
     const size_t n_prev_toks = std::min((size_t) promptCtx.repeat_last_n, promptCtx.tokens.size());
     return llama_sample_top_p_top_k(d_ptr->ctx,
         promptCtx.tokens.data() + promptCtx.tokens.size() - n_prev_toks,
-        n_prev_toks, promptCtx.top_k, promptCtx.top_p, promptCtx.temp,
-        promptCtx.repeat_penalty);
+        n_prev_toks, promptCtx.top_k, promptCtx.top_p, promptCtx.min_p, promptCtx.temp,
+        promptCtx.repeat_penalty, promptCtx.n_last_batch_tokens - 1);
 }
 
 bool LLamaModel::evalTokens(PromptContext &ctx, const std::vector<int32_t> &tokens) const
 {
-    return llama_eval(d_ptr->ctx, tokens.data(), tokens.size(), ctx.n_past, d_ptr->n_threads) == 0;
+    llama_kv_cache_seq_rm(d_ptr->ctx, 0, ctx.n_past, -1);
+
+    llama_batch batch = llama_batch_init(tokens.size(), 0, 1);
+
+    batch.n_tokens = tokens.size();
+    ctx.n_last_batch_tokens = tokens.size();
+
+    for (int32_t i = 0; i < batch.n_tokens; i++) {
+        batch.token   [i] = tokens[i];
+        batch.pos     [i] = ctx.n_past + i;
+        batch.n_seq_id[i] = 1;
+        batch.seq_id  [i][0] = 0;
+        batch.logits  [i] = false;
+    }
+
+    // llama_decode will output logits only for the last token of the prompt
+    batch.logits[batch.n_tokens - 1] = true;
+
+    int res = llama_decode(d_ptr->ctx, batch);
+    llama_batch_free(batch);
+    return res == 0;
 }
 
 int32_t LLamaModel::contextLength() const
@@ -269,69 +473,84 @@ const std::vector<LLModel::Token> &LLamaModel::endTokens() const
     return d_ptr->end_tokens;
 }
 
-#if defined(GGML_USE_KOMPUTE)
-#include "ggml-vulkan.h"
-#endif
-
-std::vector<LLModel::GPUDevice> LLamaModel::availableGPUDevices(size_t memoryRequired)
+bool LLamaModel::shouldAddBOS() const
 {
-#if defined(GGML_USE_KOMPUTE)
-    std::vector<ggml_vk_device> vkDevices = ggml_vk_available_devices(memoryRequired);
-
-    std::vector<LLModel::GPUDevice> devices;
-    for(const auto& vkDevice : vkDevices) {
-        LLModel::GPUDevice device;
-        device.index = vkDevice.index;
-        device.type = vkDevice.type;
-        device.heapSize = vkDevice.heapSize;
-        device.name = vkDevice.name;
-        device.vendor = vkDevice.vendor;
-
-        devices.push_back(device);
-    }
-
-    return devices;
-#else
-    return std::vector<LLModel::GPUDevice>();
-#endif
+    int add_bos = llama_add_bos_token(d_ptr->model);
+    if (add_bos != -1) { return add_bos; }
+    auto vocab_type = llama_vocab_type(d_ptr->model);
+    return vocab_type == LLAMA_VOCAB_TYPE_SPM || vocab_type == LLAMA_VOCAB_TYPE_WPM;
 }
 
-bool LLamaModel::initializeGPUDevice(size_t memoryRequired, const std::string& device)
+int32_t LLamaModel::maxContextLength(std::string const &modelPath) const
+{
+    return get_arch_key_u32(modelPath, "context_length");
+}
+
+int32_t LLamaModel::layerCount(std::string const &modelPath) const
+{
+    return get_arch_key_u32(modelPath, "block_count");
+}
+
+std::vector<LLModel::GPUDevice> LLamaModel::availableGPUDevices(size_t memoryRequired) const
+{
+#ifdef GGML_USE_KOMPUTE
+    size_t count = 0;
+    auto * vkDevices = ggml_vk_available_devices(memoryRequired, &count);
+
+    if (vkDevices) {
+        std::vector<LLModel::GPUDevice> devices;
+        devices.reserve(count);
+
+        for (size_t i = 0; i < count; ++i) {
+            auto & dev = vkDevices[i];
+            devices.emplace_back(
+                /* index    = */ dev.index,
+                /* type     = */ dev.type,
+                /* heapSize = */ dev.heapSize,
+                /* name     = */ dev.name,
+                /* vendor   = */ dev.vendor
+            );
+            ggml_vk_device_destroy(&dev);
+        }
+
+        free(vkDevices);
+        return devices;
+    }
+#else
+    (void)memoryRequired;
+    std::cerr << __func__ << ": built without Kompute\n";
+#endif
+
+    return {};
+}
+
+bool LLamaModel::initializeGPUDevice(size_t memoryRequired, const std::string &name) const
 {
 #if defined(GGML_USE_KOMPUTE)
-    return ggml_vk_init_device(memoryRequired, device);
+    ggml_vk_device device;
+    bool ok = ggml_vk_get_device(&device, memoryRequired, name.c_str());
+    if (ok) {
+        d_ptr->device = device.index;
+        return true;
+    }
 #else
+    (void)memoryRequired;
+    (void)name;
+#endif
     return false;
-#endif
 }
 
-bool LLamaModel::initializeGPUDevice(const LLModel::GPUDevice &device, std::string *unavail_reason)
+bool LLamaModel::initializeGPUDevice(int device, std::string *unavail_reason) const
 {
-    bool result = false;
 #if defined(GGML_USE_KOMPUTE)
-    ggml_vk_device vkDevice;
-    vkDevice.index = device.index;
-    vkDevice.type = device.type;
-    vkDevice.heapSize = device.heapSize;
-    vkDevice.name = device.name;
-    vkDevice.vendor = device.vendor;
-    result = ggml_vk_init_device(vkDevice);
-    if (!result && unavail_reason) {
-        *unavail_reason = "failed to init GPU";
-    }
+    (void)unavail_reason;
+    d_ptr->device = device;
+    return true;
 #else
+    (void)device;
     if (unavail_reason) {
         *unavail_reason = "built without Kompute";
     }
-#endif
-    return result;
-}
-
-bool LLamaModel::initializeGPUDevice(int device)
-{
-#if defined(GGML_USE_KOMPUTE)
-    return ggml_vk_init_device(device);
-#else
     return false;
 #endif
 }
@@ -339,7 +558,7 @@ bool LLamaModel::initializeGPUDevice(int device)
 bool LLamaModel::hasGPUDevice()
 {
 #if defined(GGML_USE_KOMPUTE)
-    return ggml_vk_has_device();
+    return d_ptr->device != -1;
 #else
     return false;
 #endif
@@ -348,21 +567,332 @@ bool LLamaModel::hasGPUDevice()
 bool LLamaModel::usingGPUDevice()
 {
 #if defined(GGML_USE_KOMPUTE)
-    return ggml_vk_using_vulkan();
+    return hasGPUDevice() && d_ptr->model_params.n_gpu_layers > 0;
 #elif defined(GGML_USE_METAL)
     return true;
-#endif
+#else
     return false;
+#endif
 }
 
-std::string get_arch_name(gguf_context *ctx_gguf) {
-    std::string arch_name;
-    const int kid = gguf_find_key(ctx_gguf, "general.architecture");
-    enum gguf_type ktype = gguf_get_kv_type(ctx_gguf, kid);
-    if (ktype != (GGUF_TYPE_STRING)) {
-        throw std::runtime_error("ERROR: Can't get general architecture from gguf file.");
+void llama_batch_add(
+                 struct llama_batch & batch,
+                        llama_token   id,
+                          llama_pos   pos,
+    const std::vector<llama_seq_id> & seq_ids,
+                               bool   logits) {
+    batch.token   [batch.n_tokens] = id;
+    batch.pos     [batch.n_tokens] = pos;
+    batch.n_seq_id[batch.n_tokens] = seq_ids.size();
+    for (size_t i = 0; i < seq_ids.size(); ++i) {
+        batch.seq_id[batch.n_tokens][i] = seq_ids[i];
     }
-    return gguf_get_val_str(ctx_gguf, kid);
+    batch.logits  [batch.n_tokens] = logits;
+
+    batch.n_tokens++;
+}
+
+static void batch_add_seq(llama_batch &batch, const std::vector<LLModel::Token> &tokens, int seq_id) {
+    for (unsigned i = 0; i < tokens.size(); i++) {
+        llama_batch_add(batch, tokens[i], i, { seq_id }, i == tokens.size() - 1);
+    }
+}
+
+size_t LLamaModel::embeddingSize() const {
+    return llama_n_embd(d_ptr->model);
+}
+
+struct EmbModelSpec {
+    const char *docPrefix;
+    const char *queryPrefix;
+    std::vector<const char *> otherPrefixes = {};
+    bool matryoshkaCapable = false;
+    const char *recommendedDims = nullptr;
+};
+
+struct EmbModelGroup {
+    EmbModelSpec spec;
+    std::vector<const char *> names;
+};
+
+static const EmbModelSpec NOPREFIX_SPEC {"", ""};
+static const EmbModelSpec NOMIC_SPEC    {"search_document", "search_query", {"clustering", "classification"}};
+static const EmbModelSpec E5_SPEC       {"passage", "query"};
+
+static const EmbModelSpec NOMIC_1_5_SPEC {
+    "search_document", "search_query", {"clustering", "classification"}, true, "[768, 512, 384, 256, 128]",
+};
+static const EmbModelSpec LLM_EMBEDDER_SPEC {
+    "Represent this document for retrieval",
+    "Represent this query for retrieving relevant documents",
+};
+static const EmbModelSpec BGE_SPEC {
+    "", "Represent this sentence for searching relevant passages",
+};
+static const EmbModelSpec E5_MISTRAL_SPEC {
+    "", "Instruct: Given a query, retrieve relevant passages that answer the query\nQuery",
+};
+
+static const EmbModelGroup EMBEDDING_MODEL_SPECS[] {
+    {NOPREFIX_SPEC,     {"all-MiniLM-L6-v1", "all-MiniLM-L12-v1", "all-MiniLM-L6-v2", "all-MiniLM-L12-v2"}},
+    {NOMIC_SPEC,        {"nomic-embed-text-v1", "nomic-embed-text-v1-ablated", "nomic-embed-text-v1-unsupervised"}},
+    {NOMIC_1_5_SPEC,    {"nomic-embed-text-v1.5"}},
+    {LLM_EMBEDDER_SPEC, {"llm-embedder"}},
+    {BGE_SPEC,          {"bge-small-en", "bge-base-en", "bge-large-en",
+                         "bge-small-en-v1.5", "bge-base-en-v1.5", "bge-large-en-v1.5"}},
+    // NOTE: E5 Mistral is not yet implemented in llama.cpp, so it's not in EMBEDDING_ARCHES
+    {E5_SPEC,           {"e5-small", "e5-base", "e5-large",
+                         "e5-small-unsupervised", "e5-base-unsupervised", "e5-large-unsupervised",
+                         "e5-small-v2", "e5-base-v2", "e5-large-v2"}},
+    {E5_MISTRAL_SPEC,   {"e5-mistral-7b-instruct",
+                         "multilingual-e5-small", "multilingual-e5-base", "multilingual-e5-large",
+                         "multilingual-e5-large-instruct"}},
+};
+
+static const EmbModelSpec *getEmbedSpec(const std::string &modelName) {
+    static const auto &specs = EMBEDDING_MODEL_SPECS;
+    auto it = std::find_if(specs, std::end(specs),
+        [&modelName](auto &spec) {
+            auto &names = spec.names;
+            return std::find(names.begin(), names.end(), modelName) < names.end();
+        }
+    );
+    return it < std::end(specs) ? &it->spec : nullptr;
+}
+
+void LLamaModel::embed(
+    const std::vector<std::string> &texts, float *embeddings, bool isRetrieval, int dimensionality, size_t *tokenCount,
+    bool doMean, bool atlas
+) {
+    const EmbModelSpec *spec;
+    std::optional<std::string> prefix;
+    if (d_ptr->model && (spec = getEmbedSpec(llama_model_name(d_ptr->model))))
+        prefix = isRetrieval ? spec->queryPrefix : spec->docPrefix;
+
+    embed(texts, embeddings, prefix, dimensionality, tokenCount, doMean, atlas);
+}
+
+void LLamaModel::embed(
+    const std::vector<std::string> &texts, float *embeddings, std::optional<std::string> prefix, int dimensionality,
+    size_t *tokenCount, bool doMean, bool atlas
+) {
+    if (!d_ptr->model)
+        throw std::logic_error("no model is loaded");
+
+    const char *modelName = llama_model_name(d_ptr->model);
+    if (!m_supportsEmbedding)
+        throw std::logic_error("not an embedding model: "s + modelName);
+
+    auto *spec = getEmbedSpec(modelName);
+    if (!spec)
+        std::cerr << __func__ << ": warning: unknown model " << modelName << "\n";
+
+    const int32_t n_embd = llama_n_embd(d_ptr->model);
+    if (dimensionality < 0) {
+        dimensionality = n_embd;
+    } else if (spec && dimensionality != n_embd) {
+        auto msg = [dimensionality, modelName]() {
+            return "unsupported dimensionality " + std::to_string(dimensionality) + " for model " + modelName;
+        };
+        if (!spec->matryoshkaCapable)
+            throw std::out_of_range(msg() + " (supported: " + std::to_string(n_embd) + ")");
+        if (dimensionality == 0 || dimensionality > n_embd)
+            throw std::out_of_range(msg() + " (recommended: " + spec->recommendedDims + ")");
+    }
+
+    if (!prefix) {
+        if (!spec)
+            throw std::invalid_argument("unknown model "s + modelName + ", specify a prefix if applicable or an empty string");
+        prefix = spec->docPrefix;
+    } else if (spec && prefix != spec->docPrefix && prefix != spec->queryPrefix &&
+               std::find(spec->otherPrefixes.begin(), spec->otherPrefixes.end(), *prefix) == spec->otherPrefixes.end())
+    {
+        std::stringstream ss;
+        ss << std::quoted(*prefix) << " is not a valid task type for model " << modelName;
+        throw std::invalid_argument(ss.str());
+    }
+
+    embedInternal(texts, embeddings, *prefix, dimensionality, tokenCount, doMean, atlas, spec);
+}
+
+// MD5 hash of "nomic empty"
+static const char EMPTY_PLACEHOLDER[] = "24df574ea1c998de59d5be15e769658e";
+
+auto product(double a) -> std::function<double(double)> {
+    return [a](double b) { return a * b; };
+}
+
+template <typename T>
+double getL2NormScale(T *start, T *end) {
+    double magnitude = std::sqrt(std::inner_product(start, end, start, 0.0));
+    return 1.0 / std::max(magnitude, 1e-12);
+}
+
+void LLamaModel::embedInternal(
+    const std::vector<std::string> &texts, float *embeddings, std::string prefix, int dimensionality,
+    size_t *tokenCount, bool doMean, bool atlas, const EmbModelSpec *spec
+) {
+    typedef std::vector<LLModel::Token> TokenString;
+    static constexpr int32_t atlasMaxLength = 8192;
+    static constexpr int chunkOverlap = 8; // Atlas overlaps n_batch-sized chunks of input by 8 tokens
+
+    const llama_token bos_token = llama_token_bos(d_ptr->model);
+    const llama_token eos_token = llama_token_eos(d_ptr->model);
+
+    bool useBOS = shouldAddBOS();
+    bool useEOS = llama_vocab_type(d_ptr->model) == LLAMA_VOCAB_TYPE_WPM;
+
+    // no EOS, optional BOS
+    auto tokenize = [this, useBOS, useEOS, eos_token](std::string text, TokenString &tokens, bool wantBOS) {
+        if (!text.empty() && text[0] != ' ') {
+            text = ' ' + text; // normalize for SPM - our fork of llama.cpp doesn't add a space prefix
+        }
+        wantBOS &= useBOS;
+
+        tokens.resize(text.length()+4);
+        int32_t n_tokens = llama_tokenize(d_ptr->model, text.c_str(), text.length(), tokens.data(), tokens.size(), wantBOS, false);
+        assert(useEOS == (eos_token != -1 && tokens[n_tokens - 1] == eos_token));
+        tokens.resize(n_tokens - useEOS); // erase EOS/SEP
+    };
+
+    // tokenize the texts
+    std::vector<TokenString> inputs;
+    for (unsigned i = 0; i < texts.size(); i++) {
+        auto &text = texts[i];
+        auto &inp = inputs.emplace_back();
+        tokenize(text, inp, false);
+        if (atlas && inp.size() > atlasMaxLength) {
+            if (doMean) {
+                throw std::length_error(
+                    "length of text at index " + std::to_string(i) + " is " + std::to_string(inp.size()) +
+                    " tokens which exceeds limit of " + std::to_string(atlasMaxLength)
+                );
+            }
+            inp.resize(atlasMaxLength);
+        } else if (inp.empty()) {
+            if (!atlas || !text.empty()) {
+                std::cerr << __func__ << ": warning: chunking tokenized text at index " << std::to_string(i)
+                          << " into zero tokens\n";
+            }
+            tokenize(EMPTY_PLACEHOLDER, inp, false);
+        }
+    }
+
+    // tokenize the prefix
+    TokenString prefixTokens;
+    if (prefix.empty()) {
+        prefixTokens.push_back(bos_token);
+    } else {
+        tokenize(prefix + ':', prefixTokens, true);
+    }
+
+    const uint32_t n_batch = llama_n_batch(d_ptr->ctx);
+    const uint32_t max_len = n_batch - (prefixTokens.size() + useEOS); // minus BOS/CLS and EOS/SEP
+    if (chunkOverlap >= max_len) {
+        throw std::logic_error("max chunk length of " + std::to_string(max_len) + " is smaller than overlap of " +
+                               std::to_string(chunkOverlap) + " tokens");
+    }
+
+    // split into max_len-sized chunks
+    struct split_batch { unsigned idx; TokenString batch; };
+    std::vector<split_batch> batches;
+    size_t totalTokens = 0;
+    for (unsigned i = 0; i < inputs.size(); i++) {
+        auto &input = inputs[i];
+        for (auto it = input.begin(); it < input.end(); it += max_len) {
+            if (it > input.begin()) { it -= chunkOverlap; }
+            auto end = std::min(it + max_len, input.end());
+            batches.push_back({ i, {} });
+            auto &batch = batches.back().batch;
+            batch = prefixTokens;
+            batch.insert(batch.end(), it, end);
+            totalTokens += end - it;
+            batch.push_back(eos_token);
+            if (!doMean) { break; /* limit text to one chunk */ }
+        }
+    }
+    inputs.clear();
+
+    // initialize batch
+    struct llama_batch batch = llama_batch_init(n_batch, 0, 1);
+
+    // n_texts x n_embd matrix
+    const int32_t n_embd = llama_n_embd(d_ptr->model);
+    std::vector<double> embeddingsSum(texts.size() * n_embd);
+    std::vector<int> embeddingsSumTotal(texts.size());
+    std::vector<int> queued_indices; // text indices of batches to be processed
+
+    auto decode = [this, &queued_indices, n_embd, &batch, &embeddingsSum, &embeddingsSumTotal, spec, dimensionality]() {
+        if (llama_decode(d_ptr->ctx, batch) < 0)
+            throw std::runtime_error("llama_decode failed");
+
+        for (int i = 0; i < batch.n_tokens; ++i) {
+            if (!batch.logits[i]) { continue; }
+            int i_prompt = queued_indices[batch.seq_id[i][0]];
+            auto *out = &embeddingsSum[i_prompt * n_embd];
+
+            // sequence embeddings aren't available when pooling_type is NONE
+            auto *embd = llama_get_embeddings_seq(d_ptr->ctx, batch.seq_id[i][0]);
+            if (!embd) { embd = llama_get_embeddings_ith(d_ptr->ctx, i); }
+            assert(embd);
+
+            auto *embd_end = embd + n_embd;
+
+            // layer normalization for nomic-embed-text-v1.5
+            if (spec && spec->matryoshkaCapable) {
+                // normalize mean
+                double mean = std::accumulate(embd, embd_end, 0.0) / n_embd;
+                std::transform(embd, embd_end, embd, [mean](double f){ return f - mean; });
+
+                // unbiased sample variance, with Bessel's correction
+                double variance = std::inner_product(embd, embd_end, embd, 0.0) / (n_embd - 1);
+
+                // trim to matryoshka dim
+                embd_end = embd + dimensionality;
+
+                // normalize variance
+                std::transform(embd, embd_end, embd, product(1.0 / std::sqrt(variance + 1e-5)));
+            }
+
+            // L2 norm
+            auto scale = getL2NormScale(embd, embd_end);
+            std::transform(embd, embd_end, out, out, [scale](double e, double o){ return o + scale * e; });
+            embeddingsSumTotal[i_prompt]++;
+        }
+    };
+
+    // break into batches
+    for (auto &inp: batches) {
+        // encode if at capacity
+        if (batch.n_tokens + inp.batch.size() > n_batch) {
+            decode();
+            batch.n_tokens = 0;
+            queued_indices.clear();
+        }
+
+        // add to batch
+        batch_add_seq(batch, inp.batch, queued_indices.size());
+        queued_indices.push_back(inp.idx);
+    }
+
+    // final batch
+    decode();
+
+    for (unsigned i = 0; i < texts.size(); i++) {
+        auto *embd = &embeddingsSum[i * n_embd];
+        auto *embd_end = embd + dimensionality;
+        int total = embeddingsSumTotal[i];
+
+        // average over chunks
+        std::transform(embd, embd_end, embd, product(1.0 / total));
+
+        // L2 norm and copy
+        auto scale = getL2NormScale(embd, embd_end);
+        std::transform(embd, embd_end, embeddings, product(scale));
+        embeddings += dimensionality;
+    }
+
+    if (tokenCount) { *tokenCount = totalTokens; }
 }
 
 #if defined(_WIN32)
@@ -384,23 +914,25 @@ DLL_EXPORT const char *get_build_variant() {
     return GGML_BUILD_VARIANT;
 }
 
-DLL_EXPORT bool magic_match(const char * fname) {
+DLL_EXPORT bool magic_match(const char *fname) {
+    auto * ctx = load_gguf(fname);
+    std::string arch = get_arch_name(ctx);
 
-    struct ggml_context * ctx_meta = NULL;
-    struct gguf_init_params params = {
-        /*.no_alloc = */ true,
-        /*.ctx      = */ &ctx_meta,
-    };
-    gguf_context *ctx_gguf = gguf_init_from_file(fname, params);
-    if (!ctx_gguf)
-        return false;
+    bool valid = true;
 
-    bool isValid = gguf_get_version(ctx_gguf) <= 2;
-    auto arch = get_arch_name(ctx_gguf);
-    isValid = isValid && (arch == "llama" || arch == "starcoder" || arch == "falcon");
+    if (std::find(KNOWN_ARCHES.begin(), KNOWN_ARCHES.end(), arch) == KNOWN_ARCHES.end()) {
+        // not supported by this version of llama.cpp
+        if (arch != "gptj") { // we support this via another module
+            std::cerr << __func__ << ": unsupported model architecture: " << arch << "\n";
+        }
+        valid = false;
+    }
 
-    gguf_free(ctx_gguf);
-    return isValid;
+    if (valid && is_embedding_arch(arch) && gguf_find_key(ctx, (arch + ".pooling_type").c_str()) < 0)
+        valid = false; // old pre-llama.cpp embedding model, e.g. all-MiniLM-L6-v2-f16.gguf
+
+    gguf_free(ctx);
+    return valid;
 }
 
 DLL_EXPORT LLModel *construct() {

gpt4all-backend/llamamodel_impl.h

@@ -4,44 +4,64 @@
 #ifndef LLAMAMODEL_H
 #define LLAMAMODEL_H
 
-#include <string>
 #include <functional>
+#include <memory>
+#include <string>
 #include <vector>
 #include "llmodel.h"
 
 struct LLamaPrivate;
+struct EmbModelSpec;
+
 class LLamaModel : public LLModel {
 public:
     LLamaModel();
     ~LLamaModel();
 
-    bool supportsEmbedding() const override { return false; }
-    bool supportsCompletion() const override { return true; }
-    bool loadModel(const std::string &modelPath) override;
+    bool supportsEmbedding() const override { return m_supportsEmbedding; }
+    bool supportsCompletion() const override { return m_supportsCompletion; }
+    bool loadModel(const std::string &modelPath, int n_ctx, int ngl) override;
+    bool isModelBlacklisted(const std::string &modelPath) const override;
+    bool isEmbeddingModel(const std::string &modelPath) const override;
     bool isModelLoaded() const override;
-    size_t requiredMem(const std::string &modelPath) override;
+    size_t requiredMem(const std::string &modelPath, int n_ctx, int ngl) override;
     size_t stateSize() const override;
     size_t saveState(uint8_t *dest) const override;
     size_t restoreState(const uint8_t *src) override;
     void setThreadCount(int32_t n_threads) override;
     int32_t threadCount() const override;
-    std::vector<GPUDevice> availableGPUDevices(size_t memoryRequired) override;
-    bool initializeGPUDevice(size_t memoryRequired, const std::string& device) override;
-    bool initializeGPUDevice(const GPUDevice &device, std::string *unavail_reason) override;
-    bool initializeGPUDevice(int device) override;
+    std::vector<GPUDevice> availableGPUDevices(size_t memoryRequired) const override;
+    bool initializeGPUDevice(size_t memoryRequired, const std::string &name) const override;
+    bool initializeGPUDevice(int device, std::string *unavail_reason = nullptr) const override;
     bool hasGPUDevice() override;
     bool usingGPUDevice() override;
 
+    size_t embeddingSize() const override;
+    // user-specified prefix
+    void embed(const std::vector<std::string> &texts, float *embeddings, std::optional<std::string> prefix,
+               int dimensionality = -1, size_t *tokenCount = nullptr, bool doMean = true, bool atlas = false) override;
+    // automatic prefix
+    void embed(const std::vector<std::string> &texts, float *embeddings, bool isRetrieval, int dimensionality = -1,
+               size_t *tokenCount = nullptr, bool doMean = true, bool atlas = false) override;
+
 private:
-    LLamaPrivate *d_ptr;
+    std::unique_ptr<LLamaPrivate> d_ptr;
+    bool m_supportsEmbedding = false;
+    bool m_supportsCompletion = false;
 
 protected:
-    std::vector<Token> tokenize(PromptContext &, const std::string&) const override;
-    std::string tokenToString(Token) const override;
-    Token sampleToken(PromptContext& ctx) const override;
-    bool evalTokens(PromptContext& ctx, const std::vector<int32_t> &tokens) const override;
+    std::vector<Token> tokenize(PromptContext &ctx, const std::string &str, bool special) const override;
+    std::string tokenToString(Token id) const override;
+    Token sampleToken(PromptContext &ctx) const override;
+    bool evalTokens(PromptContext &ctx, const std::vector<int32_t> &tokens) const override;
     int32_t contextLength() const override;
-    const std::vector<Token>& endTokens() const override;
+    const std::vector<Token> &endTokens() const override;
+    bool shouldAddBOS() const override;
+    int32_t maxContextLength(std::string const &modelPath) const override;
+    int32_t layerCount(std::string const &modelPath) const override;
+
+    void embedInternal(const std::vector<std::string> &texts, float *embeddings, std::string prefix, int dimensionality,
+                       size_t *tokenCount, bool doMean, bool atlas, const EmbModelSpec *spec);
 };
 
 #endif // LLAMAMODEL_H

gpt4all-backend/llmodel.cpp

@@ -2,47 +2,44 @@
 #include "dlhandle.h"
 #include "sysinfo.h"
 
-#include <iostream>
-#include <string>
-#include <vector>
-#include <fstream>
-#include <filesystem>
 #include <cassert>
 #include <cstdlib>
+#include <filesystem>
+#include <fstream>
+#include <iostream>
+#include <memory>
+#include <regex>
 #include <sstream>
+#include <string>
+#include <vector>
+
 #ifdef _MSC_VER
 #include <intrin.h>
 #endif
 
 std::string s_implementations_search_path = ".";
 
-static bool has_at_least_minimal_hardware() {
-#if defined(__x86_64__) || defined(_M_X64)
-    #ifndef _MSC_VER
-        return __builtin_cpu_supports("avx");
-    #else
-        int cpuInfo[4];
-        __cpuid(cpuInfo, 1);
-        return cpuInfo[2] & (1 << 28);
-    #endif
-#else
-    return true; // Don't know how to handle non-x86_64
-#endif
-}
+#if !(defined(__x86_64__) || defined(_M_X64))
+    // irrelevant on non-x86_64
+    #define cpu_supports_avx()  -1
+    #define cpu_supports_avx2() -1
+#elif defined(_MSC_VER)
+    // MSVC
+    static int get_cpu_info(int func_id, int reg_id) {
+        int info[4];
+        __cpuid(info, func_id);
+        return info[reg_id];
+    }
 
-static bool requires_avxonly() {
-#if defined(__x86_64__) || defined(_M_X64)
-    #ifndef _MSC_VER
-        return !__builtin_cpu_supports("avx2");
-    #else
-        int cpuInfo[4];
-        __cpuidex(cpuInfo, 7, 0);
-        return !(cpuInfo[1] & (1 << 5));
-    #endif
+    // AVX via EAX=1: Processor Info and Feature Bits, bit 28 of ECX
+    #define cpu_supports_avx()  (get_cpu_info(1, 2) & (1 << 28))
+    // AVX2 via EAX=7, ECX=0: Extended Features, bit 5 of EBX
+    #define cpu_supports_avx2() (get_cpu_info(7, 1) & (1 <<  5))
 #else
-    return false; // Don't know how to handle non-x86_64
+    // gcc/clang
+    #define cpu_supports_avx()  __builtin_cpu_supports("avx")
+    #define cpu_supports_avx2() __builtin_cpu_supports("avx2")
 #endif
-}
 
 LLModel::Implementation::Implementation(Dlhandle &&dlhandle_)
     : m_dlhandle(new Dlhandle(std::move(dlhandle_))) {
@@ -68,19 +65,30 @@ LLModel::Implementation::Implementation(Implementation &&o)
 }
 
 LLModel::Implementation::~Implementation() {
-    if (m_dlhandle) delete m_dlhandle;
+    delete m_dlhandle;
 }
 
-bool LLModel::Implementation::isImplementation(const Dlhandle &dl) {
+static bool isImplementation(const Dlhandle &dl) {
     return dl.get<bool(uint32_t)>("is_g4a_backend_model_implementation");
 }
 
 const std::vector<LLModel::Implementation> &LLModel::Implementation::implementationList() {
+    if (cpu_supports_avx() == 0) {
+        throw std::runtime_error("CPU does not support AVX");
+    }
+
     // NOTE: allocated on heap so we leak intentionally on exit so we have a chance to clean up the
     // individual models without the cleanup of the static list interfering
     static auto* libs = new std::vector<Implementation>([] () {
         std::vector<Implementation> fres;
 
+        std::string impl_name_re = "(gptj|llamamodel-mainline)";
+        if (cpu_supports_avx2() == 0) {
+            impl_name_re += "-avxonly";
+        } else {
+            impl_name_re += "-(default|metal)";
+        }
+        std::regex re(impl_name_re);
         auto search_in_directory = [&](const std::string& paths) {
             std::stringstream ss(paths);
             std::string path;
@@ -90,13 +98,15 @@ const std::vector<LLModel::Implementation> &LLModel::Implementation::implementat
                 // Iterate over all libraries
                 for (const auto& f : std::filesystem::directory_iterator(fs_path)) {
                     const std::filesystem::path& p = f.path();
+
                     if (p.extension() != LIB_FILE_EXT) continue;
+                    if (!std::regex_search(p.stem().string(), re)) continue;
+
                     // Add to list if model implementation
                     try {
                         Dlhandle dl(p.string());
-                        if (!Implementation::isImplementation(dl)) {
+                        if (!isImplementation(dl))
                             continue;
-                        }
                         fres.emplace_back(Implementation(std::move(dl)));
                     } catch (...) {}
                 }
@@ -112,20 +122,22 @@ const std::vector<LLModel::Implementation> &LLModel::Implementation::implementat
 }
 
 const LLModel::Implementation* LLModel::Implementation::implementation(const char *fname, const std::string& buildVariant) {
+    bool buildVariantMatched = false;
     for (const auto& i : implementationList()) {
         if (buildVariant != i.m_buildVariant) continue;
+        buildVariantMatched = true;
+
         if (!i.m_magicMatch(fname)) continue;
         return &i;
     }
-    return nullptr;
+
+    if (!buildVariantMatched)
+        throw std::runtime_error("Could not find any implementations for build variant: " + buildVariant);
+
+    return nullptr; // unsupported model format
 }
 
-LLModel *LLModel::Implementation::construct(const std::string &modelPath, std::string buildVariant) {
-    if (!has_at_least_minimal_hardware()) {
-        std::cerr << "LLModel ERROR: CPU does not support AVX\n";
-        return nullptr;
-    }
-
+LLModel *LLModel::Implementation::construct(const std::string &modelPath, std::string buildVariant, int n_ctx) {
     // Get correct implementation
     const Implementation* impl = nullptr;
 
@@ -136,7 +148,11 @@ LLModel *LLModel::Implementation::construct(const std::string &modelPath, std::s
             if(impl) {
                 LLModel* metalimpl = impl->m_construct();
                 metalimpl->m_implementation = impl;
-                size_t req_mem = metalimpl->requiredMem(modelPath);
+                /* TODO(cebtenzzre): after we fix requiredMem, we should change this to happen at
+                 * load time, not construct time. right now n_ctx is incorrectly hardcoded 2048 in
+                 * most (all?) places where this is called, causing underestimation of required
+                 * memory. */
+                size_t req_mem = metalimpl->requiredMem(modelPath, n_ctx, 100);
                 float req_to_total = (float) req_mem / (float) total_mem;
                 // on a 16GB M2 Mac a 13B q4_0 (0.52) works for me but a 13B q4_K_M (0.55) does not
                 if (req_to_total >= 0.53) {
@@ -147,12 +163,14 @@ LLModel *LLModel::Implementation::construct(const std::string &modelPath, std::s
                 }
             }
         }
+    #else
+        (void)n_ctx;
     #endif
 
     if (!impl) {
         //TODO: Auto-detect CUDA/OpenCL
         if (buildVariant == "auto") {
-            if (requires_avxonly()) {
+            if (cpu_supports_avx2() == 0) {
                 buildVariant = "avxonly";
             } else {
                 buildVariant = "default";
@@ -168,6 +186,54 @@ LLModel *LLModel::Implementation::construct(const std::string &modelPath, std::s
     return fres;
 }
 
+LLModel *LLModel::Implementation::constructDefaultLlama() {
+    static std::unique_ptr<LLModel> llama([]() -> LLModel * {
+        const std::vector<LLModel::Implementation> *impls;
+        try {
+            impls = &implementationList();
+        } catch (const std::runtime_error &e) {
+            std::cerr << __func__ << ": implementationList failed: " << e.what() << "\n";
+            return nullptr;
+        }
+
+        const LLModel::Implementation *impl = nullptr;
+        for (const auto &i: *impls) {
+            if (i.m_buildVariant == "metal" || i.m_modelType != "LLaMA") continue;
+            impl = &i;
+        }
+        if (!impl) {
+            std::cerr << __func__ << ": could not find llama.cpp implementation\n";
+            return nullptr;
+        }
+
+        auto fres = impl->m_construct();
+        fres->m_implementation = impl;
+        return fres;
+    }());
+    return llama.get();
+}
+
+std::vector<LLModel::GPUDevice> LLModel::Implementation::availableGPUDevices() {
+    auto *llama = constructDefaultLlama();
+    if (llama) { return llama->availableGPUDevices(0); }
+    return {};
+}
+
+int32_t LLModel::Implementation::maxContextLength(const std::string &modelPath) {
+    auto *llama = constructDefaultLlama();
+    return llama ? llama->maxContextLength(modelPath) : -1;
+}
+
+int32_t LLModel::Implementation::layerCount(const std::string &modelPath) {
+    auto *llama = constructDefaultLlama();
+    return llama ? llama->layerCount(modelPath) : -1;
+}
+
+bool LLModel::Implementation::isEmbeddingModel(const std::string &modelPath) {
+    auto *llama = constructDefaultLlama();
+    return llama && llama->isEmbeddingModel(modelPath);
+}
+
 void LLModel::Implementation::setImplementationsSearchPath(const std::string& path) {
     s_implementations_search_path = path;
 }
@@ -175,3 +241,7 @@ void LLModel::Implementation::setImplementationsSearchPath(const std::string& pa
 const std::string& LLModel::Implementation::implementationsSearchPath() {
     return s_implementations_search_path;
 }
+
+bool LLModel::Implementation::hasSupportedCPU() {
+    return cpu_supports_avx() != 0;
+}

gpt4all-backend/llmodel.h

@@ -1,13 +1,14 @@
 #ifndef LLMODEL_H
 #define LLMODEL_H
 
-#include <string>
-#include <functional>
-#include <vector>
-#include <string_view>
-#include <fstream>
 #include <cstdint>
+#include <fstream>
+#include <functional>
 #include <limits>
+#include <optional>
+#include <string>
+#include <string_view>
+#include <vector>
 
 #define LLMODEL_MAX_PROMPT_BATCH 128
 
@@ -15,28 +16,46 @@ class Dlhandle;
 class LLModel {
 public:
     using Token = int32_t;
+
+    struct GPUDevice {
+        int index;
+        int type;
+        size_t heapSize;
+        std::string name;
+        std::string vendor;
+
+        GPUDevice(int index, int type, size_t heapSize, std::string name, std::string vendor):
+            index(index), type(type), heapSize(heapSize), name(std::move(name)), vendor(std::move(vendor)) {}
+    };
+
     class Implementation {
     public:
-        Implementation(Dlhandle&&);
-        Implementation(const Implementation&) = delete;
-        Implementation(Implementation&&);
+        Implementation(const Implementation &) = delete;
+        Implementation(Implementation &&);
         ~Implementation();
 
         std::string_view modelType() const { return m_modelType; }
         std::string_view buildVariant() const { return m_buildVariant; }
 
-        static bool isImplementation(const Dlhandle&);
-        static const std::vector<Implementation>& implementationList();
-        static const Implementation *implementation(const char *fname, const std::string& buildVariant);
-        static LLModel *construct(const std::string &modelPath, std::string buildVariant = "auto");
-        static void setImplementationsSearchPath(const std::string& path);
-        static const std::string& implementationsSearchPath();
+        static LLModel *construct(const std::string &modelPath, std::string buildVariant = "auto", int n_ctx = 2048);
+        static std::vector<GPUDevice> availableGPUDevices();
+        static int32_t maxContextLength(const std::string &modelPath);
+        static int32_t layerCount(const std::string &modelPath);
+        static bool isEmbeddingModel(const std::string &modelPath);
+        static void setImplementationsSearchPath(const std::string &path);
+        static const std::string &implementationsSearchPath();
+        static bool hasSupportedCPU();
 
     private:
+        Implementation(Dlhandle &&);
+
+        static const std::vector<Implementation> &implementationList();
+        static const Implementation *implementation(const char *fname, const std::string &buildVariant);
+        static LLModel *constructDefaultLlama();
+
         bool (*m_magicMatch)(const char *fname);
         LLModel *(*m_construct)();
 
-    private:
         std::string_view m_modelType;
         std::string_view m_buildVariant;
         Dlhandle *m_dlhandle;
@@ -50,73 +69,105 @@ public:
         int32_t n_predict = 200;
         int32_t top_k = 40;
         float   top_p = 0.9f;
+        float   min_p = 0.0f;
         float   temp = 0.9f;
         int32_t n_batch = 9;
         float   repeat_penalty = 1.10f;
         int32_t repeat_last_n = 64;     // last n tokens to penalize
-        float   contextErase = 0.75f;   // percent of context to erase if we exceed the context
-            // window
+        float   contextErase = 0.75f;   // percent of context to erase if we exceed the context window
+        int32_t n_last_batch_tokens = 0;
     };
 
-    struct GPUDevice {
-        int index = 0;
-        int type = 0;
-        size_t heapSize = 0;
-        std::string name;
-        std::string vendor;
-    };
+    using ProgressCallback = std::function<bool(float progress)>;
 
     explicit LLModel() {}
     virtual ~LLModel() {}
 
     virtual bool supportsEmbedding() const = 0;
     virtual bool supportsCompletion() const = 0;
-    virtual bool loadModel(const std::string &modelPath) = 0;
+    virtual bool loadModel(const std::string &modelPath, int n_ctx, int ngl) = 0;
+    virtual bool isModelBlacklisted(const std::string &modelPath) const { (void)modelPath; return false; };
+    virtual bool isEmbeddingModel(const std::string &modelPath) const { (void)modelPath; return false; }
     virtual bool isModelLoaded() const = 0;
-    virtual size_t requiredMem(const std::string &modelPath) = 0;
+    virtual size_t requiredMem(const std::string &modelPath, int n_ctx, int ngl) = 0;
     virtual size_t stateSize() const { return 0; }
-    virtual size_t saveState(uint8_t */*dest*/) const { return 0; }
-    virtual size_t restoreState(const uint8_t */*src*/) { return 0; }
+    virtual size_t saveState(uint8_t *dest) const { (void)dest; return 0; }
+    virtual size_t restoreState(const uint8_t *src) { (void)src; return 0; }
 
     // This method requires the model to return true from supportsCompletion otherwise it will throw
     // an error
     virtual void prompt(const std::string &prompt,
+                        const std::string &promptTemplate,
                         std::function<bool(int32_t)> promptCallback,
                         std::function<bool(int32_t, const std::string&)> responseCallback,
                         std::function<bool(bool)> recalculateCallback,
-                        PromptContext &ctx);
+                        PromptContext &ctx,
+                        bool special = false,
+                        std::string *fakeReply = nullptr);
 
-    virtual std::vector<float> embedding(const std::string &text);
+    virtual size_t embeddingSize() const {
+        throw std::logic_error(std::string(implementation().modelType()) + " does not support embeddings");
+    }
+    // user-specified prefix
+    virtual void embed(const std::vector<std::string> &texts, float *embeddings, std::optional<std::string> prefix,
+                       int dimensionality = -1, size_t *tokenCount = nullptr, bool doMean = true, bool atlas = false);
+    // automatic prefix
+    virtual void embed(const std::vector<std::string> &texts, float *embeddings, bool isRetrieval,
+                       int dimensionality = -1, size_t *tokenCount = nullptr, bool doMean = true, bool atlas = false);
 
-    virtual void setThreadCount(int32_t /*n_threads*/) {}
+    virtual void setThreadCount(int32_t n_threads) { (void)n_threads; }
     virtual int32_t threadCount() const { return 1; }
 
-    const Implementation& implementation() const {
+    const Implementation &implementation() const {
         return *m_implementation;
     }
 
-    virtual std::vector<GPUDevice> availableGPUDevices(size_t /*memoryRequired*/) { return std::vector<GPUDevice>(); }
-    virtual bool initializeGPUDevice(size_t /*memoryRequired*/, const std::string& /*device*/) { return false; }
-    virtual bool initializeGPUDevice(const GPUDevice &/*device*/, std::string *unavail_reason = nullptr) {
+    virtual std::vector<GPUDevice> availableGPUDevices(size_t memoryRequired) const {
+        (void)memoryRequired;
+        return {};
+    }
+
+    virtual bool initializeGPUDevice(size_t memoryRequired, const std::string &name) const {
+        (void)memoryRequired;
+        (void)name;
+        return false;
+    }
+
+    virtual bool initializeGPUDevice(int device, std::string *unavail_reason = nullptr) const {
+        (void)device;
         if (unavail_reason) {
             *unavail_reason = "model has no GPU support";
         }
         return false;
     }
-    virtual bool initializeGPUDevice(int /*device*/) { return false; }
+
     virtual bool hasGPUDevice() { return false; }
     virtual bool usingGPUDevice() { return false; }
-    static std::vector<GPUDevice> availableGPUDevices();
+
+    void setProgressCallback(ProgressCallback callback) { m_progressCallback = callback; }
 
 protected:
     // These are pure virtual because subclasses need to implement as the default implementation of
     // 'prompt' above calls these functions
-    virtual std::vector<Token> tokenize(PromptContext &, const std::string&) const = 0;
-    virtual std::string tokenToString(Token) const = 0;
+    virtual std::vector<Token> tokenize(PromptContext &ctx, const std::string &str, bool special = false) const = 0;
+    virtual std::string tokenToString(Token id) const = 0;
     virtual Token sampleToken(PromptContext &ctx) const = 0;
-    virtual bool evalTokens(PromptContext &/*ctx*/, const std::vector<int32_t>& /*tokens*/) const = 0;
+    virtual bool evalTokens(PromptContext &ctx, const std::vector<int32_t> &tokens) const = 0;
     virtual int32_t contextLength() const = 0;
-    virtual const std::vector<Token>& endTokens() const = 0;
+    virtual const std::vector<Token> &endTokens() const = 0;
+    virtual bool shouldAddBOS() const = 0;
+
+    virtual int32_t maxContextLength(std::string const &modelPath) const
+    {
+        (void)modelPath;
+        return -1;
+    }
+
+    virtual int32_t layerCount(std::string const &modelPath) const
+    {
+        (void)modelPath;
+        return -1;
+    }
 
     // This is a helper function called from the default implementation of 'prompt' but it can be
     // shared by all base classes so it isn't virtual
@@ -124,6 +175,24 @@ protected:
 
     const Implementation *m_implementation = nullptr;
 
+    ProgressCallback m_progressCallback;
+    static bool staticProgressCallback(float progress, void* ctx)
+    {
+        LLModel* model = static_cast<LLModel*>(ctx);
+        if (model && model->m_progressCallback)
+            return model->m_progressCallback(progress);
+        return true;
+    }
+
+    void decodePrompt(std::function<bool(int32_t)> promptCallback,
+                      std::function<bool(int32_t, const std::string&)> responseCallback,
+                      std::function<bool(bool)> recalculateCallback,
+                      PromptContext &promptCtx,
+                      std::vector<Token> embd_inp);
+    void generateResponse(std::function<bool(int32_t, const std::string&)> responseCallback,
+                          std::function<bool(bool)> recalculateCallback,
+                          PromptContext &promptCtx);
+
 private:
     friend class LLMImplementation;
 };

gpt4all-backend/llmodel_c.cpp

@@ -1,8 +1,10 @@
 #include "llmodel_c.h"
 #include "llmodel.h"
 
-#include <cstring>
 #include <cerrno>
+#include <cstring>
+#include <iostream>
+#include <optional>
 #include <utility>
 
 struct LLModelWrapper {
@@ -11,121 +13,103 @@ struct LLModelWrapper {
     ~LLModelWrapper() { delete llModel; }
 };
 
-
-thread_local static std::string last_error_message;
-
-
 llmodel_model llmodel_model_create(const char *model_path) {
-    auto fres = llmodel_model_create2(model_path, "auto", nullptr);
+    const char *error;
+    auto fres = llmodel_model_create2(model_path, "auto", &error);
     if (!fres) {
-        fprintf(stderr, "Invalid model file\n");
+        fprintf(stderr, "Unable to instantiate model: %s\n", error);
     }
     return fres;
 }
 
-llmodel_model llmodel_model_create2(const char *model_path, const char *build_variant, llmodel_error *error) {
-    auto wrapper = new LLModelWrapper;
-    int error_code = 0;
+static void llmodel_set_error(const char **errptr, const char *message) {
+    thread_local static std::string last_error_message;
+    if (errptr) {
+        last_error_message = message;
+        *errptr = last_error_message.c_str();
+    }
+}
 
+llmodel_model llmodel_model_create2(const char *model_path, const char *build_variant, const char **error) {
+    LLModel *llModel;
     try {
-        wrapper->llModel = LLModel::Implementation::construct(model_path, build_variant);
+        llModel = LLModel::Implementation::construct(model_path, build_variant);
     } catch (const std::exception& e) {
-        error_code = EINVAL;
-        last_error_message = e.what();
+        llmodel_set_error(error, e.what());
+        return nullptr;
     }
 
-    if (!wrapper->llModel) {
-        delete std::exchange(wrapper, nullptr);
-        // Get errno and error message if none
-        if (error_code == 0) {
-            if (errno != 0) {
-                error_code = errno;
-                last_error_message = std::strerror(error_code);
-            } else {
-                error_code = ENOTSUP;
-                last_error_message = "Model format not supported (no matching implementation found)";
-            }
-        }
-        // Set error argument
-        if (error) {
-            error->message = last_error_message.c_str();
-            error->code = error_code;
-        }
+    if (!llModel) {
+        llmodel_set_error(error, "Model format not supported (no matching implementation found)");
+        return nullptr;
     }
-    return reinterpret_cast<llmodel_model*>(wrapper);
+
+    auto wrapper = new LLModelWrapper;
+    wrapper->llModel = llModel;
+    return wrapper;
 }
 
 void llmodel_model_destroy(llmodel_model model) {
-    delete reinterpret_cast<LLModelWrapper*>(model);
+    delete static_cast<LLModelWrapper *>(model);
 }
 
-size_t llmodel_required_mem(llmodel_model model, const char *model_path)
+size_t llmodel_required_mem(llmodel_model model, const char *model_path, int n_ctx, int ngl)
 {
-    LLModelWrapper *wrapper = reinterpret_cast<LLModelWrapper*>(model);
-    return wrapper->llModel->requiredMem(model_path);
+    auto *wrapper = static_cast<LLModelWrapper *>(model);
+    return wrapper->llModel->requiredMem(model_path, n_ctx, ngl);
 }
 
-bool llmodel_loadModel(llmodel_model model, const char *model_path)
+bool llmodel_loadModel(llmodel_model model, const char *model_path, int n_ctx, int ngl)
 {
-    LLModelWrapper *wrapper = reinterpret_cast<LLModelWrapper*>(model);
-    return wrapper->llModel->loadModel(model_path);
+    auto *wrapper = static_cast<LLModelWrapper *>(model);
+
+    std::string modelPath(model_path);
+    if (wrapper->llModel->isModelBlacklisted(modelPath)) {
+        size_t slash = modelPath.find_last_of("/\\");
+        auto basename = slash == std::string::npos ? modelPath : modelPath.substr(slash + 1);
+        std::cerr << "warning: model '" << basename << "' is out-of-date, please check for an updated version\n";
+    }
+    return wrapper->llModel->loadModel(modelPath, n_ctx, ngl);
 }
 
 bool llmodel_isModelLoaded(llmodel_model model)
 {
-    LLModelWrapper *wrapper = reinterpret_cast<LLModelWrapper*>(model);
+    auto *wrapper = static_cast<LLModelWrapper *>(model);
     return wrapper->llModel->isModelLoaded();
 }
 
 uint64_t llmodel_get_state_size(llmodel_model model)
 {
-    LLModelWrapper *wrapper = reinterpret_cast<LLModelWrapper*>(model);
+    auto *wrapper = static_cast<LLModelWrapper *>(model);
     return wrapper->llModel->stateSize();
 }
 
 uint64_t llmodel_save_state_data(llmodel_model model, uint8_t *dest)
 {
-    LLModelWrapper *wrapper = reinterpret_cast<LLModelWrapper*>(model);
+    auto *wrapper = static_cast<LLModelWrapper *>(model);
     return wrapper->llModel->saveState(dest);
 }
 
 uint64_t llmodel_restore_state_data(llmodel_model model, const uint8_t *src)
 {
-    LLModelWrapper *wrapper = reinterpret_cast<LLModelWrapper*>(model);
+    auto *wrapper = static_cast<LLModelWrapper *>(model);
     return wrapper->llModel->restoreState(src);
 }
 
-// Wrapper functions for the C callbacks
-bool prompt_wrapper(int32_t token_id, void *user_data) {
-    llmodel_prompt_callback callback = reinterpret_cast<llmodel_prompt_callback>(user_data);
-    return callback(token_id);
-}
-
-bool response_wrapper(int32_t token_id, const std::string &response, void *user_data) {
-    llmodel_response_callback callback = reinterpret_cast<llmodel_response_callback>(user_data);
-    return callback(token_id, response.c_str());
-}
-
-bool recalculate_wrapper(bool is_recalculating, void *user_data) {
-    llmodel_recalculate_callback callback = reinterpret_cast<llmodel_recalculate_callback>(user_data);
-    return callback(is_recalculating);
-}
-
 void llmodel_prompt(llmodel_model model, const char *prompt,
+                    const char *prompt_template,
                     llmodel_prompt_callback prompt_callback,
                     llmodel_response_callback response_callback,
                     llmodel_recalculate_callback recalculate_callback,
-                    llmodel_prompt_context *ctx)
+                    llmodel_prompt_context *ctx,
+                    bool special,
+                    const char *fake_reply)
 {
-    LLModelWrapper *wrapper = reinterpret_cast<LLModelWrapper*>(model);
+    auto *wrapper = static_cast<LLModelWrapper *>(model);
 
-    // Create std::function wrappers that call the C function pointers
-    std::function<bool(int32_t)> prompt_func =
-        std::bind(&prompt_wrapper, std::placeholders::_1, reinterpret_cast<void*>(prompt_callback));
-    std::function<bool(int32_t, const std::string&)> response_func =
-        std::bind(&response_wrapper, std::placeholders::_1, std::placeholders::_2, reinterpret_cast<void*>(response_callback));
-    std::function<bool(bool)> recalc_func =
-        std::bind(&recalculate_wrapper, std::placeholders::_1, reinterpret_cast<void*>(recalculate_callback));
+    auto response_func = [response_callback](int32_t token_id, const std::string &response) {
+        return response_callback(token_id, response.c_str());
+    };
 
     if (size_t(ctx->n_past) < wrapper->promptContext.tokens.size())
         wrapper->promptContext.tokens.resize(ctx->n_past);
@@ -136,14 +120,20 @@ void llmodel_prompt(llmodel_model model, const char *prompt,
     wrapper->promptContext.n_predict = ctx->n_predict;
     wrapper->promptContext.top_k = ctx->top_k;
     wrapper->promptContext.top_p = ctx->top_p;
+    wrapper->promptContext.min_p = ctx->min_p;
     wrapper->promptContext.temp = ctx->temp;
     wrapper->promptContext.n_batch = ctx->n_batch;
     wrapper->promptContext.repeat_penalty = ctx->repeat_penalty;
     wrapper->promptContext.repeat_last_n = ctx->repeat_last_n;
     wrapper->promptContext.contextErase = ctx->context_erase;
 
+    std::string fake_reply_str;
+    if (fake_reply) { fake_reply_str = fake_reply; }
+    auto *fake_reply_p = fake_reply ? &fake_reply_str : nullptr;
+
     // Call the C++ prompt method
-    wrapper->llModel->prompt(prompt, prompt_func, response_func, recalc_func, wrapper->promptContext);
+    wrapper->llModel->prompt(prompt, prompt_template, prompt_callback, response_func, recalculate_callback,
+                             wrapper->promptContext, special, fake_reply_p);
 
     // Update the C context by giving access to the wrappers raw pointers to std::vector data
     // which involves no copies
@@ -158,6 +148,7 @@ void llmodel_prompt(llmodel_model model, const char *prompt,
     ctx->n_predict = wrapper->promptContext.n_predict;
     ctx->top_k = wrapper->promptContext.top_k;
     ctx->top_p = wrapper->promptContext.top_p;
+    ctx->min_p = wrapper->promptContext.min_p;
     ctx->temp = wrapper->promptContext.temp;
     ctx->n_batch = wrapper->promptContext.n_batch;
     ctx->repeat_penalty = wrapper->promptContext.repeat_penalty;
@@ -165,38 +156,58 @@ void llmodel_prompt(llmodel_model model, const char *prompt,
     ctx->context_erase = wrapper->promptContext.contextErase;
 }
 
-float *llmodel_embedding(llmodel_model model, const char *text, size_t *embedding_size)
-{
-    if (model == nullptr || text == nullptr || !strlen(text)) {
-        *embedding_size = 0;
+float *llmodel_embed(
+    llmodel_model model, const char **texts, size_t *embedding_size, const char *prefix, int dimensionality,
+    size_t *token_count, bool do_mean, bool atlas, const char **error
+) {
+    auto *wrapper = static_cast<LLModelWrapper *>(model);
+
+    if (!texts || !*texts) {
+        llmodel_set_error(error, "'texts' is NULL or empty");
         return nullptr;
     }
-    LLModelWrapper *wrapper = reinterpret_cast<LLModelWrapper*>(model);
-    std::vector<float> embeddingVector = wrapper->llModel->embedding(text);
-    float *embedding = (float *)malloc(embeddingVector.size() * sizeof(float));
-    if (embedding == nullptr) {
-        *embedding_size = 0;
+
+    std::vector<std::string> textsVec;
+    while (*texts) { textsVec.emplace_back(*texts++); }
+
+    size_t embd_size;
+    float *embedding;
+
+    try {
+        embd_size = wrapper->llModel->embeddingSize();
+        if (dimensionality > 0 && dimensionality < int(embd_size))
+            embd_size = dimensionality;
+
+        embd_size *= textsVec.size();
+
+        std::optional<std::string> prefixStr;
+        if (prefix) { prefixStr = prefix; }
+
+        embedding = new float[embd_size];
+        wrapper->llModel->embed(textsVec, embedding, prefixStr, dimensionality, token_count, do_mean, atlas);
+    } catch (std::exception const &e) {
+        llmodel_set_error(error, e.what());
         return nullptr;
     }
-    std::copy(embeddingVector.begin(), embeddingVector.end(), embedding);
-    *embedding_size = embeddingVector.size();
+
+    *embedding_size = embd_size;
     return embedding;
 }
 
 void llmodel_free_embedding(float *ptr)
 {
-    free(ptr);
+    delete[] ptr;
 }
 
 void llmodel_setThreadCount(llmodel_model model, int32_t n_threads)
 {
-    LLModelWrapper *wrapper = reinterpret_cast<LLModelWrapper*>(model);
+    auto *wrapper = static_cast<LLModelWrapper *>(model);
     wrapper->llModel->setThreadCount(n_threads);
 }
 
 int32_t llmodel_threadCount(llmodel_model model)
 {
-    LLModelWrapper *wrapper = reinterpret_cast<LLModelWrapper*>(model);
+    auto *wrapper = static_cast<LLModelWrapper *>(model);
     return wrapper->llModel->threadCount();
 }
 
@@ -212,7 +223,7 @@ const char *llmodel_get_implementation_search_path()
 
 struct llmodel_gpu_device* llmodel_available_gpu_devices(llmodel_model model, size_t memoryRequired, int* num_devices)
 {
-    LLModelWrapper *wrapper = reinterpret_cast<LLModelWrapper*>(model);
+    auto *wrapper = static_cast<LLModelWrapper *>(model);
     std::vector<LLModel::GPUDevice> devices = wrapper->llModel->availableGPUDevices(memoryRequired);
 
     // Set the num_devices
@@ -236,30 +247,24 @@ struct llmodel_gpu_device* llmodel_available_gpu_devices(llmodel_model model, si
 
 bool llmodel_gpu_init_gpu_device_by_string(llmodel_model model, size_t memoryRequired, const char *device)
 {
-    LLModelWrapper *wrapper = reinterpret_cast<LLModelWrapper*>(model);
+    auto *wrapper = static_cast<LLModelWrapper *>(model);
     return wrapper->llModel->initializeGPUDevice(memoryRequired, std::string(device));
 }
 
 bool llmodel_gpu_init_gpu_device_by_struct(llmodel_model model, const llmodel_gpu_device *device)
 {
-    LLModel::GPUDevice d;
-    d.index = device->index;
-    d.type = device->type;
-    d.heapSize = device->heapSize;
-    d.name = device->name;
-    d.vendor = device->vendor;
-    LLModelWrapper *wrapper = reinterpret_cast<LLModelWrapper*>(model);
-    return wrapper->llModel->initializeGPUDevice(d);
+    auto *wrapper = static_cast<LLModelWrapper *>(model);
+    return wrapper->llModel->initializeGPUDevice(device->index);
 }
 
 bool llmodel_gpu_init_gpu_device_by_int(llmodel_model model, int device)
 {
-    LLModelWrapper *wrapper = reinterpret_cast<LLModelWrapper*>(model);
+    auto *wrapper = static_cast<LLModelWrapper *>(model);
     return wrapper->llModel->initializeGPUDevice(device);
 }
 
 bool llmodel_has_gpu_device(llmodel_model model)
 {
-    LLModelWrapper *wrapper = reinterpret_cast<LLModelWrapper*>(model);
+    auto *wrapper = static_cast<LLModelWrapper *>(model);
     return wrapper->llModel->hasGPUDevice();
 }

gpt4all-backend/llmodel_c.h

@@ -23,17 +23,6 @@ extern "C" {
  */
 typedef void *llmodel_model;
 
-/**
- * Structure containing any errors that may eventually occur
- */
-struct llmodel_error {
-    const char *message;  // Human readable error description; Thread-local; guaranteed to survive until next llmodel C API call
-    int code;             // errno; 0 if none
-};
-#ifndef __cplusplus
-typedef struct llmodel_error llmodel_error;
-#endif
-
 /**
  * llmodel_prompt_context structure for holding the prompt context.
  * NOTE: The implementation takes care of all the memory handling of the raw logits pointer and the
@@ -50,6 +39,7 @@ struct llmodel_prompt_context {
     int32_t n_predict;      // number of tokens to predict
     int32_t top_k;          // top k logits to sample from
     float top_p;            // nucleus sampling probability threshold
+    float min_p;            // Min P sampling
     float temp;             // temperature to adjust model's output distribution
     int32_t n_batch;        // number of predictions to generate in parallel
     float repeat_penalty;   // penalty factor for repeated tokens
@@ -105,10 +95,10 @@ DEPRECATED llmodel_model llmodel_model_create(const char *model_path);
  * Recognises correct model type from file at model_path
  * @param model_path A string representing the path to the model file; will only be used to detect model type.
  * @param build_variant A string representing the implementation to use (auto, default, avxonly, ...),
- * @param error A pointer to a llmodel_error; will only be set on error.
+ * @param error A pointer to a string; will only be set on error.
  * @return A pointer to the llmodel_model instance; NULL on error.
  */
-llmodel_model llmodel_model_create2(const char *model_path, const char *build_variant, llmodel_error *error);
+llmodel_model llmodel_model_create2(const char *model_path, const char *build_variant, const char **error);
 
 /**
  * Destroy a llmodel instance.
@@ -121,17 +111,21 @@ void llmodel_model_destroy(llmodel_model model);
  * Estimate RAM requirement for a model file
  * @param model A pointer to the llmodel_model instance.
  * @param model_path A string representing the path to the model file.
+ * @param n_ctx Maximum size of context window
+ * @param ngl Number of GPU layers to use (Vulkan)
  * @return size greater than 0 if the model was parsed successfully, 0 if file could not be parsed.
  */
-size_t llmodel_required_mem(llmodel_model model, const char *model_path);
+size_t llmodel_required_mem(llmodel_model model, const char *model_path, int n_ctx, int ngl);
 
 /**
  * Load a model from a file.
  * @param model A pointer to the llmodel_model instance.
  * @param model_path A string representing the path to the model file.
+ * @param n_ctx Maximum size of context window
+ * @param ngl Number of GPU layers to use (Vulkan)
  * @return true if the model was loaded successfully, false otherwise.
  */
-bool llmodel_loadModel(llmodel_model model, const char *model_path);
+bool llmodel_loadModel(llmodel_model model, const char *model_path, int n_ctx, int ngl);
 
 /**
  * Check if a model is loaded.
@@ -170,29 +164,46 @@ uint64_t llmodel_restore_state_data(llmodel_model model, const uint8_t *src);
  * Generate a response using the model.
  * @param model A pointer to the llmodel_model instance.
  * @param prompt A string representing the input prompt.
+ * @param prompt_template A string representing the input prompt template.
  * @param prompt_callback A callback function for handling the processing of prompt.
  * @param response_callback A callback function for handling the generated response.
  * @param recalculate_callback A callback function for handling recalculation requests.
+ * @param special True if special tokens in the prompt should be processed, false otherwise.
+ * @param fake_reply A string to insert into context as the model's reply, or NULL to generate one.
  * @param ctx A pointer to the llmodel_prompt_context structure.
  */
 void llmodel_prompt(llmodel_model model, const char *prompt,
+                    const char *prompt_template,
                     llmodel_prompt_callback prompt_callback,
                     llmodel_response_callback response_callback,
                     llmodel_recalculate_callback recalculate_callback,
-                    llmodel_prompt_context *ctx);
+                    llmodel_prompt_context *ctx,
+                    bool special,
+                    const char *fake_reply);
 
 /**
  * Generate an embedding using the model.
  * NOTE: If given NULL pointers for the model or text, or an empty text, a NULL pointer will be
  * returned. Bindings should signal an error when NULL is the return value.
  * @param model A pointer to the llmodel_model instance.
- * @param text A string representing the text to generate an embedding for.
+ * @param texts A pointer to a NULL-terminated array of strings representing the texts to generate an
+ * embedding for.
  * @param embedding_size A pointer to a size_t type that will be set by the call indicating the length
  * of the returned floating point array.
+ * @param prefix The model-specific prefix representing the embedding task, without the trailing colon. NULL for no
+ * prefix.
+ * @param dimensionality The embedding dimension, for use with Matryoshka-capable models. Set to -1 to for full-size.
+ * @param token_count Return location for the number of prompt tokens processed, or NULL.
+ * @param do_mean True to average multiple embeddings if the text is longer than the model can accept, False to
+ * truncate.
+ * @param atlas Try to be fully compatible with the Atlas API. Currently, this means texts longer than 8192 tokens with
+ * long_text_mode="mean" will raise an error. Disabled by default.
+ * @param error Return location for a malloc()ed string that will be set on error, or NULL.
  * @return A pointer to an array of floating point values passed to the calling method which then will
- * be responsible for lifetime of this memory.
+ * be responsible for lifetime of this memory. NULL if an error occurred.
  */
-float *llmodel_embedding(llmodel_model model, const char *text, size_t *embedding_size);
+float *llmodel_embed(llmodel_model model, const char **texts, size_t *embedding_size, const char *prefix,
+                     int dimensionality, size_t *token_count, bool do_mean, bool atlas, const char **error);
 
 /**
  * Frees the memory allocated by the llmodel_embedding function.

gpt4all-backend/llmodel_shared.cpp

@@ -2,15 +2,21 @@
 
 #include <cassert>
 #include <iostream>
+#include <regex>
+#include <string>
 #include <unordered_set>
 
-#ifdef GGML_USE_KOMPUTE
-#include "ggml-vulkan.h"
-#endif
-
+// TODO(cebtenzzre): replace this with llama_kv_cache_seq_shift for llamamodel (GPT-J needs this as-is)
 void LLModel::recalculateContext(PromptContext &promptCtx, std::function<bool(bool)> recalculate) {
-    size_t i = 0;
-    promptCtx.n_past = 0;
+    int n_keep = shouldAddBOS();
+    const int32_t n_discard = (promptCtx.n_ctx - n_keep) * promptCtx.contextErase;
+
+    // Erase the first percentage of context from the tokens
+    std::cerr << implementation().modelType() << ": reached the end of the context window so resizing\n";
+    promptCtx.tokens.erase(promptCtx.tokens.begin() + n_keep, promptCtx.tokens.begin() + n_keep + n_discard);
+
+    size_t i = n_keep;
+    promptCtx.n_past = n_keep;
     while (i < promptCtx.tokens.size()) {
         size_t batch_end = std::min(i + promptCtx.n_batch, promptCtx.tokens.size());
         std::vector<int32_t> batch(promptCtx.tokens.begin() + i, promptCtx.tokens.begin() + batch_end);
@@ -30,11 +36,36 @@ stop_generating:
     recalculate(false);
 }
 
+static bool parsePromptTemplate(const std::string &tmpl, std::vector<std::smatch> &placeholders, std::string &err) {
+    static const std::regex placeholderRegex(R"(%[1-2](?![0-9]))");
+
+    auto it = std::sregex_iterator(tmpl.begin(), tmpl.end(), placeholderRegex);
+    placeholders.clear();
+    placeholders.insert(placeholders.end(), it, std::sregex_iterator());
+
+    if (placeholders.size() > 2) {
+        err = "ERROR: expected at most two placeholders, got " + std::to_string(placeholders.size());
+        return false;
+    }
+    if (placeholders.size() >= 1 && placeholders[0].str() != "%1") {
+        err = "ERROR: first placeholder must be %1, got " + placeholders[0].str();
+        return false;
+    }
+    if (placeholders.size() >= 2 && placeholders[1].str() != "%2") {
+        err = "ERROR: second placeholder must be %2, got " + placeholders[1].str();
+        return false;
+    }
+    return true;
+}
+
 void LLModel::prompt(const std::string &prompt,
+                     const std::string &promptTemplate,
                      std::function<bool(int32_t)> promptCallback,
                      std::function<bool(int32_t, const std::string&)> responseCallback,
                      std::function<bool(bool)> recalculateCallback,
-                     PromptContext &promptCtx)
+                     PromptContext &promptCtx,
+                     bool special,
+                     std::string *fakeReply)
 {
     if (!isModelLoaded()) {
         std::cerr << implementation().modelType() << " ERROR: prompt won't work with an unloaded model!\n";
@@ -42,15 +73,89 @@ void LLModel::prompt(const std::string &prompt,
     }
 
     if (!supportsCompletion()) {
-        std::string errorMessage = "ERROR: this model does not support text completion or chat!\n";
+        std::string errorMessage = "ERROR: this model does not support text completion or chat!";
         responseCallback(-1, errorMessage);
-        std::cerr << implementation().modelType() << errorMessage;
+        std::cerr << implementation().modelType() << " " << errorMessage << "\n";
         return;
     }
 
-    // tokenize the prompt
-    std::vector<Token> embd_inp = tokenize(promptCtx, prompt);
+    // parse the prompt template
+    std::vector<std::smatch> placeholders;
+    {
+        std::string err;
+        if (!parsePromptTemplate(promptTemplate, placeholders, err)) {
+            responseCallback(-1, err);
+            std::cerr << err << "\n";
+            return;
+        }
+    }
 
+    auto old_n_past = promptCtx.n_past; // prepare to fake n_past for tokenize
+
+    // tokenize the user prompt
+    std::vector<Token> embd_inp;
+    if (placeholders.empty()) {
+        // this is unusual, but well-defined
+        std::cerr << __func__ << ": prompt template has no placeholder\n";
+        embd_inp = tokenize(promptCtx, promptTemplate, true);
+    } else {
+        // template: beginning of user prompt
+        const auto &phUser = placeholders[0];
+        std::string userPrefix(phUser.prefix());
+        if (!userPrefix.empty()) {
+            embd_inp = tokenize(promptCtx, userPrefix, true);
+            promptCtx.n_past += embd_inp.size();
+        }
+
+        // user input (shouldn't have special token processing)
+        auto tokens = tokenize(promptCtx, prompt, special);
+        embd_inp.insert(embd_inp.end(), tokens.begin(), tokens.end());
+        promptCtx.n_past += tokens.size();
+
+        // template: end of user prompt + start of assistant prompt
+        size_t start = phUser.position() + phUser.length();
+        size_t end = placeholders.size() >= 2 ? placeholders[1].position() : promptTemplate.length();
+        auto userToAsst = promptTemplate.substr(start, end - start);
+        if (!userToAsst.empty()) {
+            tokens = tokenize(promptCtx, userToAsst, true);
+            embd_inp.insert(embd_inp.end(), tokens.begin(), tokens.end());
+            promptCtx.n_past += tokens.size();
+        }
+    }
+
+    promptCtx.n_past = old_n_past; // restore n_past so decodePrompt can increment it
+
+    // decode the user prompt
+    decodePrompt(promptCallback, responseCallback, recalculateCallback, promptCtx, embd_inp);
+
+    // decode the assistant's reply, either generated or spoofed
+    if (fakeReply == nullptr) {
+        generateResponse(responseCallback, recalculateCallback, promptCtx);
+    } else {
+        embd_inp = tokenize(promptCtx, *fakeReply, false);
+        decodePrompt(promptCallback, responseCallback, recalculateCallback, promptCtx, embd_inp);
+    }
+
+    // decode the rest of the prompt template
+    // template: end of assistant prompt
+    std::string asstSuffix;
+    if (placeholders.size() >= 2) {
+        size_t start = placeholders[1].position() + placeholders[1].length();
+        asstSuffix = promptTemplate.substr(start);
+    } else {
+        asstSuffix = "\n\n"; // default to a blank link, good for e.g. Alpaca
+    }
+    if (!asstSuffix.empty()) {
+        embd_inp = tokenize(promptCtx, asstSuffix, true);
+        decodePrompt(promptCallback, responseCallback, recalculateCallback, promptCtx, embd_inp);
+    }
+}
+
+void LLModel::decodePrompt(std::function<bool(int32_t)> promptCallback,
+                           std::function<bool(int32_t, const std::string&)> responseCallback,
+                           std::function<bool(bool)> recalculateCallback,
+                           PromptContext &promptCtx,
+                           std::vector<Token> embd_inp) {
     // save the context size
     promptCtx.n_ctx = contextLength();
 
@@ -73,11 +178,6 @@ void LLModel::prompt(const std::string &prompt,
 
         // Check if the context has run out...
         if (promptCtx.n_past + int32_t(batch.size()) > promptCtx.n_ctx) {
-            const int32_t erasePoint = promptCtx.n_ctx * promptCtx.contextErase;
-            // Erase the first percentage of context from the tokens...
-            std::cerr << implementation().modelType() << ": reached the end of the context window so resizing\n";
-            promptCtx.tokens.erase(promptCtx.tokens.begin(), promptCtx.tokens.begin() + erasePoint);
-            promptCtx.n_past = promptCtx.tokens.size();
             recalculateContext(promptCtx, recalculateCallback);
             assert(promptCtx.n_past + int32_t(batch.size()) <= promptCtx.n_ctx);
         }
@@ -98,7 +198,11 @@ void LLModel::prompt(const std::string &prompt,
         }
         i = batch_end;
     }
+}
 
+void LLModel::generateResponse(std::function<bool(int32_t, const std::string&)> responseCallback,
+                               std::function<bool(bool)> recalculateCallback,
+                               PromptContext &promptCtx) {
     std::string cachedResponse;
     std::vector<Token> cachedTokens;
     std::unordered_set<std::string> reversePrompts
@@ -112,11 +216,6 @@ void LLModel::prompt(const std::string &prompt,
 
         // Check if the context has run out...
         if (promptCtx.n_past + 1 > promptCtx.n_ctx) {
-            const int32_t erasePoint = promptCtx.n_ctx * promptCtx.contextErase;
-            // Erase the first percentage of context from the tokens...
-            std::cerr << implementation().modelType() << ": reached the end of the context window so resizing\n";
-            promptCtx.tokens.erase(promptCtx.tokens.begin(), promptCtx.tokens.begin() + erasePoint);
-            promptCtx.n_past = promptCtx.tokens.size();
             recalculateContext(promptCtx, recalculateCallback);
             assert(promptCtx.n_past + 1 <= promptCtx.n_ctx);
         }
@@ -169,34 +268,30 @@ void LLModel::prompt(const std::string &prompt,
     }
 }
 
-std::vector<float> LLModel::embedding(const std::string &/*text*/)
-{
-    if (!supportsCompletion()) {
-        std::string errorMessage = "ERROR: this model does not support generating embeddings!\n";
-        std::cerr << implementation().modelType() << errorMessage;
-    }
-    return std::vector<float>();
+void LLModel::embed(
+    const std::vector<std::string> &texts, float *embeddings, std::optional<std::string> prefix, int dimensionality,
+    size_t *tokenCount, bool doMean, bool atlas
+) {
+    (void)texts;
+    (void)embeddings;
+    (void)prefix;
+    (void)dimensionality;
+    (void)tokenCount;
+    (void)doMean;
+    (void)atlas;
+    throw std::logic_error(std::string(implementation().modelType()) + " does not support embeddings");
 }
 
-std::vector<LLModel::GPUDevice> LLModel::availableGPUDevices()
-{
-#if defined(GGML_USE_KOMPUTE)
-    std::vector<ggml_vk_device> vkDevices = ggml_vk_available_devices(0);
-
-    std::vector<LLModel::GPUDevice> devices;
-    for(const auto& vkDevice : vkDevices) {
-        LLModel::GPUDevice device;
-        device.index = vkDevice.index;
-        device.type = vkDevice.type;
-        device.heapSize = vkDevice.heapSize;
-        device.name = vkDevice.name;
-        device.vendor = vkDevice.vendor;
-
-        devices.push_back(device);
-    }
-
-    return devices;
-#else
-    return std::vector<LLModel::GPUDevice>();
-#endif
+void LLModel::embed(
+    const std::vector<std::string> &texts, float *embeddings, bool isRetrieval, int dimensionality, size_t *tokenCount,
+    bool doMean, bool atlas
+) {
+    (void)texts;
+    (void)embeddings;
+    (void)isRetrieval;
+    (void)dimensionality;
+    (void)tokenCount;
+    (void)doMean;
+    (void)atlas;
+    throw std::logic_error(std::string(implementation().modelType()) + " does not support embeddings");
 }

gpt4all-backend/llmodel_shared.h

@@ -4,49 +4,6 @@
 #include <vector>
 #include <ggml.h>
 
-#if defined(GGML_USE_KOMPUTE)
-#include "ggml-vulkan.h"
-struct llm_buffer {
-    uint8_t * addr = NULL;
-    size_t size = 0;
-    ggml_vk_memory memory;
-
-    llm_buffer() = default;
-
-    void resize(size_t size) {
-        free();
-
-        if (!ggml_vk_has_device()) {
-            this->addr = new uint8_t[size];
-            this->size = size;
-        } else {
-            this->memory = ggml_vk_allocate(size);
-            this->addr = (uint8_t*)memory.data;
-            this->size = size;
-        }
-    }
-
-    void free() {
-        if (!memory.primaryMemory) {
-            delete[] addr;
-        } else if (memory.data) {
-            ggml_vk_free_memory(memory);
-        }
-        this->addr = NULL;
-        this->size = 0;
-    }
-
-    ~llm_buffer() {
-        free();
-    }
-
-    // disable copy and move
-    llm_buffer(const llm_buffer&) = delete;
-    llm_buffer(llm_buffer&&) = delete;
-    llm_buffer& operator=(const llm_buffer&) = delete;
-    llm_buffer& operator=(llm_buffer&&) = delete;
-};
-#else
 struct llm_buffer {
     uint8_t * addr = NULL;
     size_t size = 0;
@@ -61,7 +18,6 @@ struct llm_buffer {
         delete[] addr;
     }
 };
-#endif
 
 struct llm_kv_cache {
     struct ggml_tensor * k;

gpt4all-backend/mpt.cpp

@@ -1,969 +0,0 @@
-#define MPT_H_I_KNOW_WHAT_I_AM_DOING_WHEN_INCLUDING_THIS_FILE
-#include "mpt_impl.h"
-
-#include "utils.h"
-#include "llmodel_shared.h"
-
-#include <cassert>
-#include <cinttypes>
-#include <cmath>
-#include <cstdio>
-#include <cstring>
-#include <map>
-#include <random>
-#include <string>
-#include <vector>
-#include <iostream>
-#if defined(_WIN32) && defined(_MSC_VER)
-    #define WIN32_LEAN_AND_MEAN
-    #ifndef NOMINMAX
-        #define NOMINMAX
-    #endif
-    #include <windows.h>
-    #include <io.h>
-    #include <stdio.h>
-#else
-    #include <unistd.h>
-#endif
-#include <sstream>
-#include <thread>
-#include <unordered_set>
-#include <unordered_map>
-#include <regex>
-#include <ggml.h>
-
-
-namespace {
-const char *modelType_ = "MPT";
-}
-
-// default hparams (MPT 7B)
-struct mpt_hparams {
-    int32_t n_vocab      = 50432;
-    int32_t n_ctx        = 2048;
-    int32_t n_embd       = 4096;
-    int32_t n_head       = 32;
-    int32_t n_layer      = 32;
-    float alibi_bias_max = 8;
-    float clip_qkv       = 0;
-    float norm_eps       = 1e-5;
-    int32_t expand       = 4;
-};
-
-struct mpt_layer {
-    // normalization
-    struct ggml_tensor * norm_1_w;
-    struct ggml_tensor * norm_2_w;
-
-    // attention
-    struct ggml_tensor * attn_Wqkv_w;
-    struct ggml_tensor * attn_out_proj_w;
-
-    // ff
-    struct ggml_tensor * ffn_up_proj_w;
-    struct ggml_tensor * ffn_down_proj_w;
-};
-
-struct mpt_model {
-    mpt_hparams hparams;
-
-    // normalization
-    struct ggml_tensor * norm_f_w;
-
-    struct ggml_tensor * wte; // position embedding
-
-    // mpt does weight tying
-
-    std::vector<mpt_layer> layers;
-
-    struct llm_kv_cache kv_self;
-    struct ggml_context * ctx;
-
-
-    llm_buffer eval_buf;
-    llm_buffer scr0_buf;
-    llm_buffer scr1_buf;
-
-    ~mpt_model() {
-        if (ctx) {
-            ggml_free(ctx);
-        }
-    }
-};
-
-enum mpt_token_type {
-    MPT_TOKEN_TYPE_NORMAL  = 1,
-    MPT_TOKEN_TYPE_CONTROL = 3,
-};
-
-using replit_piece_t = std::pair<std::size_t, float>;
-using replit_piece_map_t = std::unordered_map<std::string, replit_piece_t>;
-
-static const std::string replit_ws_symbol = "\342\226\201";
-
-struct mpt_vocab {
-    bool is_replit = false;
-    gpt_vocab raw;
-    replit_piece_map_t piece_map;
-    std::vector<std::string> vocab;
-
-    const char * end_of_text() const {
-        return is_replit ? "<|endoftext|>" : "<|im_end|>";
-    }
-};
-
-std::pair<std::vector<LLModel::Token>, float> encode_word(const std::string & word, const replit_piece_map_t & model) {
-    std::vector<int> best_segmentations_starts(word.length() + 1, -1);
-    best_segmentations_starts[0] = 0;
-
-    std::vector<float> best_segmentations_scores(word.length() + 1, -std::numeric_limits<float>::infinity());
-    best_segmentations_scores[0] = 1.0;
-
-    for (size_t start_idx = 0; start_idx < word.length(); ++start_idx) {
-        float best_score_at_start = best_segmentations_scores[start_idx];
-        for (size_t end_idx = start_idx + 1; end_idx <= word.length(); ++end_idx) {
-            std::string token = word.substr(start_idx, end_idx - start_idx);
-            if (model.count(token) && best_score_at_start != -std::numeric_limits<float>::infinity()) {
-                float token_score = model.at(token).second;
-                float score = token_score + best_score_at_start;
-                if (best_segmentations_scores[end_idx] == -std::numeric_limits<float>::infinity() ||
-                    best_segmentations_scores[end_idx] > score) {
-                    best_segmentations_starts[end_idx] = start_idx;
-                    best_segmentations_scores[end_idx] = score;
-                }
-            }
-        }
-    }
-
-    if (best_segmentations_scores.back() == -std::numeric_limits<float>::infinity()) {
-        return std::make_pair(std::vector<LLModel::Token>{0}, 0.0f);
-    }
-
-    float score = best_segmentations_scores.back();
-    int start = best_segmentations_starts.back();
-    int end = word.length();
-    std::vector<LLModel::Token> tokens;
-    while (start != 0) {
-        const auto token_id = model.at(word.substr(start, end - start)).first;
-        tokens.insert(tokens.begin(), token_id);
-        int next_start = best_segmentations_starts[start];
-        end = start;
-        start = next_start;
-    }
-    const auto token_id = model.at(word.substr(start, end - start)).first;
-    tokens.insert(tokens.begin(), token_id);
-    return std::make_pair(tokens, score);
-}
-
-bool replit_tokenizer_load(mpt_vocab & tokenizer, gguf_context * ggufctx, int tokens_keyidx, int max_vocab_size) {
-    int scores_keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.scores");
-    if (scores_keyidx == -1) {
-        fprintf(stderr, "%s: llama token scores not found!\n", __func__);
-        return false;
-    }
-    const auto *scores = reinterpret_cast<const float *>(gguf_get_arr_data(ggufctx, scores_keyidx));
-
-    for (LLModel::Token i = 0; i < max_vocab_size; i++) {
-        std::string word = gguf_get_arr_str(ggufctx, tokens_keyidx, i);
-        tokenizer.piece_map[word] = std::make_pair(i, -scores[i]);
-        tokenizer.raw.id_to_token[i] = word;
-        tokenizer.raw.token_to_id[word] = i;
-    }
-
-    return true;
-}
-
-std::string replace_all(const std::string & str,    // where to work
-                        const std::string & find,   // substitute 'find'
-                        const std::string & replace //      by 'replace'
-) {
-    std::string result;
-    size_t find_len = find.size();
-    size_t pos, from = 0;
-    while (std::string::npos != (pos = str.find(find, from))) {
-        result.append(str, from, pos - from);
-        result.append(replace);
-        from = pos + find_len;
-    }
-    result.append(str, from, std::string::npos);
-    return result;
-}
-
-std::vector<LLModel::Token> replit_tokenizer_tokenize(mpt_vocab & tokenizer, const std::string & text) {
-    std::vector<LLModel::Token> tokens;
-    auto normalized_text = replace_all(text, " ", replit_ws_symbol);
-    auto tokenized = encode_word(normalized_text, tokenizer.piece_map);
-
-    return tokenized.first;
-}
-
-std::string replit_tokenizer_detokenize(mpt_vocab & tokenizer, const std::vector<LLModel::Token> & tokens) {
-    std::string text;
-    for (auto token : tokens) {
-        text += tokenizer.raw.id_to_token[token];
-    }
-    return replace_all(text, replit_ws_symbol, " ");
-}
-
-
-static bool kv_cache_init(
-        const struct mpt_hparams & hparams,
-             struct llm_kv_cache & cache,
-                         ggml_type   wtype,
-                               int   n_ctx) {
-    const int n_embd  = hparams.n_embd;
-    const int n_layer = hparams.n_layer;
-
-    const int64_t n_mem      = (int64_t)n_layer*n_ctx;
-    const int64_t n_elements = n_embd*n_mem;
-
-    cache.buf.resize(2u*n_elements*ggml_type_size(wtype) + 2_MiB);
-
-    struct ggml_init_params params;
-    params.mem_size   = cache.buf.size;
-    params.mem_buffer = cache.buf.addr;
-    params.no_alloc   = false;
-
-    cache.ctx = ggml_init(params);
-
-    if (!cache.ctx) {
-        fprintf(stderr, "%s: failed to allocate memory for kv cache\n", __func__);
-        return false;
-    }
-
-    cache.k = ggml_new_tensor_1d(cache.ctx, wtype, n_elements);
-    cache.v = ggml_new_tensor_1d(cache.ctx, wtype, n_elements);
-
-    return true;
-}
-
-// load the model's weights from a file path. if mem_req ptr is passed the model is
-// only partially parsed to estimate required memory
-bool mpt_model_load(const std::string &fname, mpt_model & model, mpt_vocab & vocab, size_t * mem_req) {
-    printf("%s: loading model from '%s' - please wait ...\n", __func__, fname.c_str());
-    if (mem_req != nullptr) {
-        *mem_req = 0;
-    }
-
-    // create the ggml context
-    struct gguf_init_params params = {
-        /*.no_alloc = */ false,
-        /*.ctx      = */ &model.ctx,
-    };
-
-    gguf_context *ggufctx = gguf_init_from_file(fname.c_str(), params);
-    if (!ggufctx) {
-        fprintf(stderr, "%s: gguf_init_from_file() failed\n", __func__);
-        return false;
-    }
-
-    printf("%s: gguf version     = %d\n", __func__, gguf_get_version(ggufctx));
-    printf("%s: gguf alignment   = %zu\n", __func__, gguf_get_alignment(ggufctx));
-    printf("%s: gguf data offset = %zu\n", __func__, gguf_get_data_offset(ggufctx));
-
-    // print some standard metadata
-    {
-        int keyidx;
-
-        keyidx = gguf_find_key(ggufctx, "general.name");
-        if (keyidx != -1) { printf("%s: model name           = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
-        keyidx = gguf_find_key(ggufctx, "general.description");
-        if (keyidx != -1) { printf("%s: model description    = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
-        keyidx = gguf_find_key(ggufctx, "general.author");
-        if (keyidx != -1) { printf("%s: model author         = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
-        keyidx = gguf_find_key(ggufctx, "general.license");
-        if (keyidx != -1) { printf("%s: model license        = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
-        keyidx = gguf_find_key(ggufctx, "general.architecture");
-        if (keyidx != -1) { printf("%s: model architecture   = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
-        keyidx = gguf_find_key(ggufctx, "general.file_type");
-        if (keyidx != -1) { printf("%s: model file type      = %" PRIu32 "\n", __func__, gguf_get_val_u32(ggufctx, keyidx)); }
-        keyidx = gguf_find_key(ggufctx, "gptneox.tensor_data_layout");
-        if (keyidx != -1) { printf("%s: model data layout    = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
-        keyidx = gguf_find_key(ggufctx, "general.source.huggingface.repository");
-        if (keyidx != -1) { printf("%s: model source HF repo = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
-    }
-
-    // check required metadata
-    {
-        // check model architecture kv
-        int keyidx = gguf_find_key(ggufctx, "general.architecture");
-        if (keyidx == -1) {
-            fprintf(stderr, "%s: gguf model architecture not found!\n", __func__);
-            return false;
-        }
-        if (strcmp(gguf_get_val_str(ggufctx, keyidx), "mpt") != 0) {
-            fprintf(stderr, "%s: model architecture not supported!\n", __func__);
-            return false;
-        }
-    }
-
-    // load hparams
-    {
-        auto & hparams = model.hparams;
-
-        bool ok = false;
-        int keyidx;
-
-        do {
-            keyidx = gguf_find_key(ggufctx, "mpt.context_length");
-            if (keyidx == -1) { break; }
-            hparams.n_ctx = gguf_get_val_u32(ggufctx, keyidx);
-
-            keyidx = gguf_find_key(ggufctx, "mpt.embedding_length");
-            if (keyidx == -1) { break; }
-            hparams.n_embd = gguf_get_val_u32(ggufctx, keyidx);
-
-            keyidx = gguf_find_key(ggufctx, "mpt.attention.head_count");
-            if (keyidx == -1) { break; }
-            hparams.n_head = gguf_get_val_u32(ggufctx, keyidx);
-
-            keyidx = gguf_find_key(ggufctx, "mpt.block_count");
-            if (keyidx == -1) { break; }
-            hparams.n_layer = gguf_get_val_u32(ggufctx, keyidx);
-
-            keyidx = gguf_find_key(ggufctx, "mpt.attention.max_alibi_bias");
-            if (keyidx == -1) { break; }
-            hparams.alibi_bias_max = gguf_get_val_f32(ggufctx, keyidx);
-
-            keyidx = gguf_find_key(ggufctx, "mpt.attention.clamp_kqv");
-            if (keyidx != -1) { // optional
-                hparams.clip_qkv = gguf_get_val_f32(ggufctx, keyidx);
-            }
-
-            keyidx = gguf_find_key(ggufctx, "mpt.attention.layer_norm_epsilon");
-            if (keyidx == -1) { break; }
-            hparams.norm_eps = gguf_get_val_f32(ggufctx, keyidx);
-
-            ok = true;
-        } while (false);
-
-        if (!ok) {
-            fprintf(stderr, "%s: required hparam missing!\n", __func__);
-            return false;
-        }
-
-        printf("%s: n_ctx          = %d\n", __func__, hparams.n_ctx);
-        printf("%s: n_embd         = %d\n", __func__, hparams.n_embd);
-        printf("%s: n_head         = %d\n", __func__, hparams.n_head);
-        printf("%s: n_layer        = %d\n", __func__, hparams.n_layer);
-        printf("%s: alibi_bias_max = %f\n", __func__, hparams.alibi_bias_max);
-        printf("%s: clip_qkv       = %f\n", __func__, hparams.clip_qkv);
-    }
-
-    // load vocab
-    {
-        auto & hparams = model.hparams;
-
-        int tokens_keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.tokens");
-        if (tokens_keyidx == -1) {
-            fprintf(stderr, "%s: tokenizer vocab not found!\n", __func__);
-            return false;
-        }
-
-        int keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.model");
-        if (keyidx == -1) {
-            fprintf(stderr, "%s: tokenizer model not found!\n", __func__);
-            return false;
-        }
-        std::string tokenizer_model(gguf_get_val_str(ggufctx, keyidx));
-
-        hparams.n_vocab = gguf_get_arr_n(ggufctx, tokens_keyidx);
-        printf("%s: %s tokenizer vocab = %d\n", __func__, tokenizer_model.c_str(), int(hparams.n_vocab));
-
-        if (tokenizer_model == "llama") { // Replit
-            vocab.is_replit = true;
-            if (!replit_tokenizer_load(vocab, ggufctx, tokens_keyidx, hparams.n_vocab)) {
-                return false;
-            }
-        } else if (tokenizer_model == "gpt2") {
-            int toktypes_keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.token_type");
-            if (toktypes_keyidx == -1) {
-                fprintf(stderr, "%s: gpt2 token types not found!\n", __func__);
-                return false;
-            }
-            const auto *toktypes = reinterpret_cast<const uint32_t *>(gguf_get_arr_data(ggufctx, toktypes_keyidx));
-
-            for (int i = 0; i < hparams.n_vocab; i++) {
-                std::string word = gguf_get_arr_str(ggufctx, tokens_keyidx, i);
-
-                bool special = false;
-                if (toktypes[i] == MPT_TOKEN_TYPE_CONTROL) {
-                    special = true;
-                } else if (toktypes[i] != MPT_TOKEN_TYPE_NORMAL) {
-                    fprintf(stderr, "%s: unknown token type: %d\n", __func__, int(toktypes[i]));
-                    return false;
-                }
-
-                vocab.raw.token_to_id[word] = i;
-                vocab.raw.id_to_token[i] = word;
-
-                if (special) {
-                    vocab.raw.add_special_token(word);
-                }
-            }
-        } else {
-            fprintf(stderr, "%s: tokenizer model not supported!\n", __func__);
-            return false;
-        }
-    }
-
-    auto & ctx = model.ctx;
-
-    size_t ctx_size = ggml_get_mem_size(ctx);
-    printf("%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size / (1024.0 * 1024.0));
-
-    if (mem_req != nullptr) {
-        *mem_req = ctx_size;
-        gguf_free(ggufctx);
-        return false;
-    }
-
-    // prepare memory for the weights
-    {
-        const auto & hparams = model.hparams;
-        model.layers.resize(hparams.n_layer);
-
-        model.wte      = ggml_get_tensor(ctx, "token_embd.weight");
-        model.norm_f_w = ggml_get_tensor(ctx, "output_norm.weight");
-
-        auto name = [](int i, std::string n) {
-            static std::string key;
-            key = "blk." + std::to_string(i) + "." + n;
-            return key.c_str();
-        };
-
-        for (int i = 0; i < hparams.n_layer; ++i) {
-            auto &layer = model.layers[i];
-
-            layer.norm_1_w        = ggml_get_tensor(ctx, name(i, "attn_norm.weight"));
-            layer.norm_2_w        = ggml_get_tensor(ctx, name(i, "ffn_norm.weight"));
-
-            layer.attn_Wqkv_w     = ggml_get_tensor(ctx, name(i, "attn_qkv.weight"));
-            layer.attn_out_proj_w = ggml_get_tensor(ctx, name(i, "attn_output.weight"));
-            layer.ffn_up_proj_w   = ggml_get_tensor(ctx, name(i, "ffn_up.weight"));
-            layer.ffn_down_proj_w = ggml_get_tensor(ctx, name(i, "ffn_down.weight"));
-        }
-    }
-
-    // key + value memory
-    {
-        const auto &hparams = model.hparams;
-        if (!kv_cache_init(hparams, model.kv_self, GGML_TYPE_F16, model.hparams.n_ctx)) {
-            fprintf(stderr, "%s: kv_cache_init() failed for self-attention cache\n", __func__);
-            ggml_free(ctx);
-            return false;
-        }
-
-        const size_t memory_size = ggml_nbytes(model.kv_self.k) + ggml_nbytes(model.kv_self.v);
-        printf("%s: kv self size  = %7.2f MB\n", __func__, memory_size / 1024.0 / 1024.0);
-    }
-
-    model.scr0_buf.resize(256u * 1024 * 1024);
-    model.scr1_buf.resize(256u * 1024 * 1024);
-
-    return true;
-}
-
-bool mpt_eval(
-        mpt_model & model,
-        const int n_threads,
-        const int n_past,
-        const std::vector<int>           & embd_inp,
-              std::vector<float>         & embd_w,
-              size_t                     & mem_per_token) {
-    const int N = embd_inp.size();
-
-    const auto & hparams = model.hparams;
-
-    const int n_embd  = hparams.n_embd;
-    const int n_layer = hparams.n_layer;
-    const int n_ctx   = hparams.n_ctx;
-    const int n_head  = hparams.n_head;
-    const int n_vocab = hparams.n_vocab;
-
-    const size_t init_buf_size = 1024_MiB;
-    if (!model.eval_buf.addr || model.eval_buf.size < init_buf_size)
-        model.eval_buf.resize(init_buf_size);
-
-    if (mem_per_token > 0 && mem_per_token*N > model.eval_buf.size) {
-        const size_t buf_size_new = 1.1*(mem_per_token*N); // add 10% to account for ggml object overhead
-        // printf("\n%s: reallocating buffer from %zu to %zu bytes\n", __func__, model.buf.size, buf_size_new);
-
-        // reallocate
-        model.eval_buf.resize(buf_size_new);
-        if (model.eval_buf.addr == nullptr) {
-            fprintf(stderr, "%s: failed to allocate %zu bytes\n", __func__, model.eval_buf.size);
-            return false;
-        }
-    }
-
-    struct ggml_init_params params = {
-        .mem_size   = model.eval_buf.size,
-        .mem_buffer = model.eval_buf.addr,
-        .no_alloc = false
-    };
-
-    struct ggml_context * ctx0 = ggml_init(params);
-    struct ggml_cgraph gf = {};
-
-    struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
-    memcpy(embd->data, embd_inp.data(), N*ggml_element_size(embd));
-
-    // wte
-    struct ggml_tensor * inpL = ggml_get_rows(ctx0, model.wte, embd);
-
-    for (int il = 0; il < n_layer; ++il) {
-        ggml_set_scratch(ctx0, {0, model.scr0_buf.size, model.scr0_buf.addr, });
-
-        struct ggml_tensor * inpSA = inpL;
-        struct ggml_tensor * cur = inpSA;
-        // self-attention
-        {
-
-            // norm1
-            cur = ggml_norm(ctx0, cur, model.hparams.norm_eps);
-            cur = ggml_mul(ctx0,
-                    ggml_repeat(ctx0, model.layers[il].norm_1_w, cur),
-                    cur);
-            // compute QKV
-            cur = ggml_mul_mat(ctx0,
-                    model.layers[il].attn_Wqkv_w,
-                    cur);
-
-            // TODO: clip_qkv
-            struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 0*ggml_element_size(cur)*n_embd));
-            struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 1*ggml_element_size(cur)*n_embd));
-            struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 2*ggml_element_size(cur)*n_embd));
-
-            // TODO: qk_ln? (seems to be False in MPT-7B configs)
-            {
-                Vcur = ggml_transpose(ctx0, Vcur);
-
-                struct ggml_tensor * k = ggml_view_1d(ctx0, model.kv_self.k, N*n_embd, (ggml_element_size(model.kv_self.k)*n_embd)*(il*n_ctx + n_past));
-                struct ggml_tensor * v = ggml_view_2d(ctx0, model.kv_self.v, N, n_embd,
-                                        (   n_ctx)*ggml_element_size(model.kv_self.v),
-                                        (il*n_ctx)*ggml_element_size(model.kv_self.v)*n_embd + n_past*ggml_element_size(model.kv_self.v));
-
-                ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Kcur, k));
-                ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Vcur, v));
-            }
-            // Q = Qcur.contiguous().view(n_embd/n_head, n_head, N).permute(0, 2, 1, 3)
-            struct ggml_tensor * Q =
-                ggml_permute(ctx0,
-                        ggml_reshape_3d(ctx0, Qcur, n_embd/n_head, n_head, N),
-                        0, 2, 1, 3);
-
-            struct ggml_tensor * K =
-                ggml_permute(ctx0,
-                        ggml_reshape_3d(ctx0,
-                            ggml_view_1d(ctx0, model.kv_self.k, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.kv_self.k)*n_embd),
-                            n_embd/n_head, n_head, n_past + N),
-                        0, 2, 1, 3);
-
-            // K * Q
-            struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
-
-            // KQ_scaled = KQ / sqrt(n_embd/n_head)
-            struct ggml_tensor * KQ_scaled =
-                ggml_scale(ctx0,
-                        KQ,
-                        ggml_new_f32(ctx0, 1.0f/sqrt(float(n_embd)/n_head))
-                        );
-
-
-            // Alibi
-            struct ggml_tensor * KQ_scaled_biased = ggml_alibi(
-                ctx0, ggml_cont(ctx0, KQ_scaled), n_past, n_head, model.hparams.alibi_bias_max
-            );
-
-            // KQ_masked = mask_past(KQ_scaled)
-            struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled_biased, n_past);
-
-            // KQ = soft_max(KQ_masked)
-            struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
-
-            // V_trans = Vmem.view(n_embd/n_head, n_head, n_past + N).permute(1, 2, 0, 3).contiguous()
-            struct ggml_tensor * V =
-                ggml_view_3d(ctx0, model.kv_self.v,
-                        n_past + N, n_embd/n_head, n_head,
-                        n_ctx*ggml_element_size(model.kv_self.v),
-                        n_ctx*ggml_element_size(model.kv_self.v)*n_embd/n_head,
-                        il*n_ctx*ggml_element_size(model.kv_self.v)*n_embd);
-
-            // KQV = transpose(V) * KQ_soft_max
-            struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
-
-            // KQV_merged = KQV.permute(0, 2, 1, 3)
-            struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
-
-            // cur = KQV_merged.contiguous().view(n_embd, N)
-            cur = ggml_cpy(ctx0,
-                    KQV_merged,
-                    ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));
-
-            // projection (no bias)
-            cur = ggml_mul_mat(ctx0,
-                    model.layers[il].attn_out_proj_w,
-                    cur);
-        }
-
-        ggml_set_scratch(ctx0, {0, model.scr1_buf.size, model.scr1_buf.addr, });
-        // residual
-        struct ggml_tensor * resSA = ggml_add(ctx0, cur, inpSA);
-        // feed-forward network
-        {
-            cur = resSA;
-            // norm2
-            cur = ggml_norm(ctx0, cur, model.hparams.norm_eps);
-            cur = ggml_mul(ctx0,
-                    ggml_repeat(ctx0, model.layers[il].norm_2_w, cur),
-                    cur);
-            // ffn
-            cur = ggml_mul_mat(ctx0,
-                    model.layers[il].ffn_up_proj_w,
-                    cur);
-            cur = ggml_gelu(ctx0, cur);
-            cur = ggml_mul_mat(ctx0,
-                    model.layers[il].ffn_down_proj_w,
-                    cur);
-
-        }
-
-        // self-attention + FF
-        inpL = ggml_add(ctx0, cur, resSA);
-    }
-    ggml_set_scratch(ctx0, {0, model.scr0_buf.size, model.scr0_buf.addr, });
-
-    struct ggml_tensor * out = inpL;
-    // -> logits
-    {
-        out = ggml_norm(ctx0, out, model.hparams.norm_eps);
-        out = ggml_mul(ctx0,
-                    ggml_repeat(ctx0, model.norm_f_w, out),
-                    out);
-        ggml_set_scratch(ctx0, { 0, 0, nullptr, });
-        out = ggml_mul_mat(ctx0, model.wte, out);
-    }
-
-    ggml_build_forward_expand(&gf, out);
-
-
-    // run the computation
-    {
-        std::unique_ptr<uint8_t []> data;
-        auto plan = ggml_graph_plan(&gf, n_threads);
-        if (plan.work_size > 0) {
-            data.reset(new uint8_t[plan.work_size]);
-            plan.work_data = data.get();
-        }
-        ggml_graph_compute(&gf, &plan);
-    }
-
-
-    // return result for just the last token
-    embd_w.resize(n_vocab);
-    memcpy(embd_w.data(), (float *) ggml_get_data(out) + (n_vocab*(N-1)), sizeof(float)*n_vocab);
-
-    if (mem_per_token == 0) {
-        mem_per_token = ggml_used_mem(ctx0)/N;
-    }
-    //printf("used_mem = %zu\n", ggml_used_mem(ctx0));
-
-    ggml_free(ctx0);
-
-    return true;
-}
-
-
-#define MPT_MAX_RNG_STATE 64*1024
-
-size_t mpt_get_state_size(const mpt_model &model)
-{
-    // we don't know size of rng until we actually serialize it. so reserve more than enough memory for its serialized state.
-    // for reference, std::mt19937(1337) serializes to 6701 bytes.
-    const size_t s_rng_size        = sizeof(size_t);
-    const size_t s_rng             = MPT_MAX_RNG_STATE;
-    const size_t s_kv_size         = sizeof(size_t);
-    const size_t s_kv_ntok         = sizeof(int);
-    const size_t s_kv              = model.kv_self.buf.size;
-    const size_t s_total = (
-        + s_rng_size
-        + s_rng
-        + s_kv_size
-        + s_kv_ntok
-        + s_kv
-    );
-    fflush(stdout);
-    return s_total;
-}
-
-size_t mpt_copy_state_data(const mpt_model &model, const std::mt19937 &rng, uint8_t *dest)
-{
-    uint8_t * out = dest;
-    fflush(stdout);
-    // copy rng
-    {
-        std::stringstream rng_ss;
-        rng_ss << rng;
-
-        const size_t rng_size = rng_ss.str().size();
-        char rng_buf[MPT_MAX_RNG_STATE];
-
-        memset(&rng_buf[0], 0, MPT_MAX_RNG_STATE);
-        memcpy(&rng_buf[0], rng_ss.str().data(), rng_ss.str().size());
-
-        memcpy(out, &rng_size,   sizeof(rng_size));   out += sizeof(rng_size);
-        memcpy(out, &rng_buf[0], MPT_MAX_RNG_STATE); out += MPT_MAX_RNG_STATE;
-    }
-
-    // copy kv cache
-    {
-        const size_t kv_size = model.kv_self.buf.size;
-        const int    kv_ntok = model.kv_self.n;
-
-        memcpy(out, &kv_size, sizeof(kv_size)); out += sizeof(kv_size);
-        memcpy(out, &kv_ntok, sizeof(kv_ntok)); out += sizeof(kv_ntok);
-
-        if (kv_size) {
-            memcpy(out, model.kv_self.buf.addr, kv_size); out += kv_size;
-        }
-    }
-
-    const size_t written  = out - dest;
-    assert(written == mpt_get_state_size(model));
-    fflush(stdout);
-    return written;
-}
-
-size_t mpt_set_state_data(mpt_model *model, std::mt19937 *rng, const uint8_t *src)
-{
-    const uint8_t * in = src;
-
-    // set rng
-    {
-        size_t rng_size;
-        char   rng_buf[MPT_MAX_RNG_STATE];
-
-        memcpy(&rng_size,   in, sizeof(rng_size));    in += sizeof(rng_size);
-        memcpy(&rng_buf[0], in, MPT_MAX_RNG_STATE); in += MPT_MAX_RNG_STATE;
-
-        std::stringstream rng_ss;
-        rng_ss.str(std::string(&rng_buf[0], rng_size));
-        rng_ss >> *rng;
-
-        assert(rng_ss.fail() == false);
-    }
-
-    // set kv cache
-    {
-        size_t kv_size;
-        int kv_ntok;
-
-        memcpy(&kv_size, in, sizeof(kv_size)); in += sizeof(kv_size);
-        memcpy(&kv_ntok, in, sizeof(kv_ntok)); in += sizeof(kv_ntok);
-
-        if (kv_size) {
-            assert(model->kv_self.buf.size == kv_size);
-
-            void * k_data = model->kv_self.k->data; // remember data pointers
-            void * v_data = model->kv_self.v->data; // because their value is stored in buf and overwritten by memcpy
-
-            memcpy(model->kv_self.buf.addr, in, kv_size); in += kv_size;
-
-            model->kv_self.k->data = k_data; // restore correct data pointers
-            model->kv_self.v->data = v_data;
-
-        }
-
-        model->kv_self.n = kv_ntok;
-    }
-
-    const size_t nread    = in - src;
-    assert(nread == mpt_get_state_size(*model));
-    fflush(stdout);
-    return nread;
-}
-
-struct MPTPrivate {
-    const std::string modelPath;
-    bool modelLoaded;
-    mpt_vocab vocab;
-    mpt_model *model = nullptr;
-    int64_t n_threads = 0;
-    size_t mem_per_token = 0;
-    std::mt19937 rng;
-    bool has_end_of_text = false;
-};
-
-MPT::MPT()
-    : d_ptr(new MPTPrivate) {
-    d_ptr->model = new mpt_model;
-    d_ptr->model->ctx = nullptr;
-    d_ptr->modelLoaded = false;
-}
-
-size_t MPT::requiredMem(const std::string &modelPath) {
-    mpt_model dummy_model;
-    mpt_vocab dummy_vocab;
-    size_t mem_req;
-    mpt_model_load(modelPath, dummy_model, dummy_vocab, &mem_req);
-    return mem_req;
-}
-
-bool MPT::loadModel(const std::string &modelPath) {
-    std::mt19937 rng(time(NULL));
-    d_ptr->rng = rng;
-
-    // load the model
-    if (!mpt_model_load(modelPath, *d_ptr->model, d_ptr->vocab, nullptr)) {
-        std::cerr << "MPT ERROR: failed to load model from " <<  modelPath;
-        return false;
-    }
-
-    d_ptr->n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
-    d_ptr->modelLoaded = true;
-    const auto & vocab = d_ptr->vocab;
-    d_ptr->has_end_of_text = vocab.raw.token_to_id.find(vocab.end_of_text()) != vocab.raw.token_to_id.end();
-    fflush(stdout);
-    return true;
-}
-
-void MPT::setThreadCount(int32_t n_threads) {
-    d_ptr->n_threads = n_threads;
-}
-
-int32_t MPT::threadCount() const
-{
-    return d_ptr->n_threads;
-}
-
-MPT::~MPT()
-{
-    delete d_ptr->model;
-}
-
-bool MPT::isModelLoaded() const
-{
-    return d_ptr->modelLoaded;
-}
-
-size_t MPT::stateSize() const
-{
-    return mpt_get_state_size(*d_ptr->model);
-}
-
-size_t MPT::saveState(uint8_t *dest) const
-{
-    return mpt_copy_state_data(*d_ptr->model, d_ptr->rng, dest);
-}
-
-size_t MPT::restoreState(const uint8_t *src)
-{
-    return mpt_set_state_data(d_ptr->model, &d_ptr->rng, src);
-}
-
-std::vector<LLModel::Token> MPT::tokenize(PromptContext &, const std::string &str) const
-{
-    if (d_ptr->vocab.is_replit) {
-        return replit_tokenizer_tokenize(d_ptr->vocab, str);
-    }
-    return ::gpt_tokenize(d_ptr->vocab.raw, str);
-}
-
-std::string MPT::tokenToString(Token id) const
-{
-    if (d_ptr->vocab.is_replit) {
-        return replit_tokenizer_detokenize(d_ptr->vocab, {id});
-    }
-    return d_ptr->vocab.raw.id_to_token[id];
-}
-
-LLModel::Token MPT::sampleToken(PromptContext &promptCtx) const
-{
-    const size_t n_prev_toks = std::min((size_t) promptCtx.repeat_last_n, promptCtx.tokens.size());
-    return gpt_sample_top_k_top_p(d_ptr->model->hparams.n_vocab,
-        promptCtx.tokens.data() + promptCtx.tokens.size() - n_prev_toks,
-        n_prev_toks,
-        promptCtx.logits,
-        promptCtx.top_k, promptCtx.top_p, promptCtx.temp,
-        promptCtx.repeat_penalty,
-        d_ptr->rng);
-}
-
-bool MPT::evalTokens(PromptContext &ctx, const std::vector<int32_t> &tokens) const
-{
-    // determine the required inference memory per token:
-    static bool initialized = false;
-    if (!initialized) {
-        mpt_eval(*d_ptr->model, d_ptr->n_threads, 0, { 0, 1, 2, 3 }, ctx.logits,
-            d_ptr->mem_per_token);
-        initialized = true;
-    }
-
-    return mpt_eval(*d_ptr->model, d_ptr->n_threads, ctx.n_past, tokens, ctx.logits, d_ptr->mem_per_token);
-}
-
-int32_t MPT::contextLength() const
-{
-    return d_ptr->model->hparams.n_ctx;
-}
-
-const std::vector<LLModel::Token> &MPT::endTokens() const
-{
-    static std::vector<LLModel::Token> fres;
-    if (fres.empty()) {
-        fres = {0, d_ptr->vocab.raw.token_to_id[d_ptr->vocab.end_of_text()]};
-    }
-    return fres;
-}
-
-std::string get_arch_name(gguf_context *ctx_gguf) {
-    std::string arch_name;
-    const int kid = gguf_find_key(ctx_gguf, "general.architecture");
-    enum gguf_type ktype = gguf_get_kv_type(ctx_gguf, kid);
-    if (ktype != GGUF_TYPE_STRING) {
-        throw std::runtime_error("ERROR: Can't get general architecture from gguf file.");
-    }
-    return gguf_get_val_str(ctx_gguf, kid);
-}
-
-#if defined(_WIN32)
-#define DLL_EXPORT __declspec(dllexport)
-#else
-#define DLL_EXPORT __attribute__ ((visibility ("default")))
-#endif
-
-extern "C" {
-DLL_EXPORT bool is_g4a_backend_model_implementation() {
-    return true;
-}
-
-DLL_EXPORT const char *get_model_type() {
-    return modelType_;
-}
-
-DLL_EXPORT const char *get_build_variant() {
-    return GGML_BUILD_VARIANT;
-}
-
-DLL_EXPORT bool magic_match(const char * fname) {
-    struct ggml_context * ctx_meta = NULL;
-    struct gguf_init_params params = {
-        /*.no_alloc = */ true,
-        /*.ctx      = */ &ctx_meta,
-    };
-    gguf_context *ctx_gguf = gguf_init_from_file(fname, params);
-    if (!ctx_gguf)
-        return false;
-
-    bool isValid = gguf_get_version(ctx_gguf) <= 2;
-    isValid = isValid && get_arch_name(ctx_gguf) == "mpt";
-
-    gguf_free(ctx_gguf);
-    return isValid;
-}
-
-DLL_EXPORT LLModel *construct() {
-    return new MPT;
-}
-}

gpt4all-backend/mpt_impl.h

@@ -1,41 +0,0 @@
-#ifndef MPT_H_I_KNOW_WHAT_I_AM_DOING_WHEN_INCLUDING_THIS_FILE
-#error This file is NOT meant to be included outside of mpt.cpp. Doing so is DANGEROUS. Be sure to know what you are doing before proceeding to #define MPT_H_I_KNOW_WHAT_I_AM_DOING_WHEN_INCLUDING_THIS_FILE
-#endif
-#ifndef MPT_H
-#define MPT_H
-
-#include <string>
-#include <functional>
-#include <vector>
-#include "llmodel.h"
-
-struct MPTPrivate;
-class MPT : public LLModel {
-public:
-    MPT();
-    ~MPT();
-
-    bool supportsEmbedding() const override { return false; }
-    bool supportsCompletion() const override { return true; }
-    bool loadModel(const std::string &modelPath) override;
-    bool isModelLoaded() const override;
-    size_t requiredMem(const std::string &modelPath) override;
-    size_t stateSize() const override;
-    size_t saveState(uint8_t *dest) const override;
-    size_t restoreState(const uint8_t *src) override;
-    void setThreadCount(int32_t n_threads) override;
-    int32_t threadCount() const override;
-
-private:
-    MPTPrivate *d_ptr;
-
-protected:
-    std::vector<Token> tokenize(PromptContext &, const std::string&) const override;
-    std::string tokenToString(Token) const override;
-    Token sampleToken(PromptContext &ctx) const override;
-    bool evalTokens(PromptContext &ctx, const std::vector<int32_t> &tokens) const override;
-    int32_t contextLength() const override;
-    const std::vector<Token>& endTokens() const override;
-};
-
-#endif // MPT_H

gpt4all-backend/scripts/convert_gptj_to_gguf.py

@@ -27,7 +27,7 @@ from pathlib import Path
 
 import gguf
 import numpy as np
-from transformers import AutoTokenizer, GPTJConfig, GPTJForCausalLM
+from transformers import AutoConfig, AutoTokenizer, GPTJForCausalLM
 from transformers.models.gpt2 import tokenization_gpt2
 
 
@@ -63,7 +63,7 @@ gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH])
 
 print("gguf: get model metadata")
 
-config = GPTJConfig(dir_model)
+config = AutoConfig.from_pretrained(dir_model)
 
 block_count = config.n_layer
 gguf_writer.add_name("GPT-J")

gpt4all-backend/scripts/convert_mpt_hf_to_gguf.py

@@ -1,162 +0,0 @@
-#!/usr/bin/env python3
-# Convert Hugging Face fine-tuned bloom-like models to ggml format
-#
-# Usage:
-#
-#   python3 models/convert-h5-to-ggml.py 
-#
-# This script is similar to "convert-pt-to-ggml.py"
-#
-
-from __future__ import annotations
-
-import json
-import struct
-import sys
-from pathlib import Path
-
-import gguf
-import numpy as np
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig
-from transformers.models.gpt2 import tokenization_gpt2
-
-
-if not 3 <= len(sys.argv) < 5:
-    print("Usage: {} model-name dir-output [ftype]".format(Path(__file__).name))
-    print("  model-name: name of the model to convert. Example: 'bigscience/bloomz-560m'")
-    print("  dir-output: directory where the output file will be written")
-    print("  ftype == 0 -> float32")
-    print("  ftype == 1 -> float16")
-    sys.exit(1)
-
-model_name = sys.argv[1]
-dir_out = Path(sys.argv[2])
-
-# make sure the output directory exists
-dir_out.mkdir(exist_ok=True)
-
-# possible data types
-#   ftype == 0 -> float32
-#   ftype == 1 -> float16
-#
-# map from ftype to string
-ftype_str = ["f32", "f16"]
-
-ftype = 1
-if len(sys.argv) > 3:
-    ftype = int(sys.argv[3])
-    if ftype < 0 or ftype > 1:
-        print("Invalid ftype: " + str(ftype))
-        sys.exit(1)
-
-fname_out = dir_out / f"ggml-model-{Path(model_name).name}-{ftype_str[ftype]}.gguf"
-
-
-ARCH = gguf.MODEL_ARCH.MPT
-gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH])
-
-print("gguf: get model metadata")
-
-config = AutoConfig.from_pretrained(model_name)
-
-block_count = config.n_layers
-gguf_writer.add_name("MPT")
-gguf_writer.add_context_length(config.max_seq_len)
-gguf_writer.add_embedding_length(config.d_model)
-gguf_writer.add_block_count(block_count)
-gguf_writer.add_feed_forward_length(4 * config.d_model)
-gguf_writer.add_head_count(config.n_heads)
-gguf_writer.add_max_alibi_bias(config.attn_config.alibi_bias_max)
-gguf_writer.add_layer_norm_eps(config.layer_norm_epsilon)
-gguf_writer.add_file_type(ftype)
-
-clip_qkv = config.attn_config.clip_qkv
-if clip_qkv is not None:
-    gguf_writer.add_clamp_kqv(clip_qkv)
-
-print("gguf: get gpt2 tokenizer vocab")
-
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-special_ids = tokenizer.all_special_ids
-
-reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
-added_tokens = tokenizer.get_added_vocab().values()
-byte_encoder = tokenization_gpt2.bytes_to_unicode()
-byte_decoder = {v: k for k, v in byte_encoder.items()}
-
-tokens: list[bytearray] = []
-toktypes: list[gguf.TokenType] = []
-
-# The number of tokens in tokenizer.json can differ from the expected vocab size.
-# This causes downstream issues with mismatched tensor sizes when running the inference
-for i in range(config.vocab_size):
-    if i not in reverse_vocab:
-        print(f"Key {i} not in tokenizer vocabulary. Padding with an arbitrary token.")
-        pad_token = f"[PAD{i}]".encode("utf8")
-        text = bytearray(pad_token)
-    elif i in added_tokens:
-        # these tokens are not encoded, for some reason
-        text = bytearray(reverse_vocab[i].encode('utf-8'))
-    else:
-        text = bytearray([byte_decoder[c] for c in reverse_vocab[i]])
-
-    tokens.append(text)
-
-    # TODO(cebtenzzre): is there a better way to do this?
-    toktypes.append(gguf.TokenType.CONTROL if i in special_ids else gguf.TokenType.NORMAL)
-
-gguf_writer.add_tokenizer_model("gpt2")
-gguf_writer.add_token_list(tokens)
-gguf_writer.add_token_types(toktypes)
-
-print("gguf: get tensor metadata")
-
-print("Loading model:", model_name)
-model = AutoModelForCausalLM.from_pretrained(
-    model_name, config=config, torch_dtype=torch.float16 if ftype == 1 else torch.float32, low_cpu_mem_usage=True,
-)
-print("Model loaded:", model_name)
-
-tensor_map = gguf.get_tensor_name_map(ARCH, block_count)
-
-list_vars = model.state_dict()
-for name in list_vars.keys():
-    data = list_vars[name].squeeze().numpy()
-    print("Processing variable:", name, "with shape:", data.shape)
-
-    n_dims = len(data.shape)
-
-    # ftype == 0 -> float32, ftype == 1 -> float16
-    ftype_cur = 0
-    # Keep token embeddings in fp32
-    if ftype == 1 and name[-7:] == ".weight" and n_dims == 2 and ".wte" not in name:
-        print("  Converting to float16")
-        data = data.astype(np.float16)
-        ftype_cur = 1
-    elif ftype == 1 or data.dtype != np.float32:
-        print("  Converting to float32")
-        data = data.astype(np.float32)
-        ftype_cur = 0
-
-    # map tensor names
-    new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
-    if new_name is None:
-        print("Can not map tensor '" + name + "'")
-        sys.exit()
-
-    gguf_writer.add_tensor(new_name, data)
-
-
-print("gguf: write header")
-gguf_writer.write_header_to_file()
-print("gguf: write metadata")
-gguf_writer.write_kv_data_to_file()
-print("gguf: write tensors")
-gguf_writer.write_tensors_to_file()
-
-gguf_writer.close()
-
-print(f"gguf: model successfully exported to '{fname_out}'")
-print()

gpt4all-backend/scripts/convert_replit_v1_hf_to_gguf.py

@@ -1,145 +0,0 @@
-#!/usr/bin/env python3
-from __future__ import annotations
-
-import json
-import struct
-import sys
-from pathlib import Path
-
-import gguf
-import numpy as np
-from sentencepiece import SentencePieceProcessor
-from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
-
-
-if not 2 <= len(sys.argv) < 4:
-    print("Usage: {} dir-model [ftype]\n".format(Path(__file__).name))
-    print("  ftype == 0 -> float32")
-    print("  ftype == 1 -> float16")
-    sys.exit(1)
-
-# output in the same directory as the model
-dir_model = Path(sys.argv[1])
-
-# possible data types
-#   ftype == 0 -> float32
-#   ftype == 1 -> float16
-#
-# map from ftype to string
-ftype_str = ["f32", "f16"]
-
-ftype = 1
-if len(sys.argv) > 2:
-    ftype = int(sys.argv[2])
-    if ftype < 0 or ftype > 1:
-        print("Invalid ftype: " + str(ftype))
-        sys.exit(1)
-
-fname_out = dir_model / ("ggml-replit-code-v1-3b-" + ftype_str[ftype] + ".gguf")
-
-
-ARCH = gguf.MODEL_ARCH.MPT
-gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH])
-
-print("gguf: get model metadata")
-
-config = AutoConfig.from_pretrained(dir_model)
-
-block_count = config.n_layers
-gguf_writer.add_name("Replit")
-gguf_writer.add_context_length(config.max_seq_len)
-gguf_writer.add_embedding_length(config.d_model)
-gguf_writer.add_block_count(block_count)
-gguf_writer.add_feed_forward_length(4 * config.d_model)
-gguf_writer.add_head_count(config.n_heads)
-gguf_writer.add_max_alibi_bias(config.attn_config.alibi_bias_max)
-gguf_writer.add_layer_norm_eps(config.layer_norm_epsilon)
-gguf_writer.add_file_type(ftype)
-
-clip_qkv = config.attn_config.clip_qkv
-if clip_qkv is not None:
-    gguf_writer.add_clamp_kqv(clip_qkv)
-
-print("gguf: get sentencepiece tokenizer vocab")
-
-tokenizer = SentencePieceProcessor(str(dir_model / "spiece.model"))
-#print(tokenizer.encode('I believe the meaning of life is'))
-
-tokens: list[bytearray] = []
-scores: list[float] = []
-toktypes: list[int] = []
-
-for i in range(tokenizer.vocab_size()):
-    tokens.append(tokenizer.id_to_piece(i).encode('utf-8'))
-    scores.append(tokenizer.get_score(i))
-
-    toktype = gguf.TokenType.NORMAL
-    if tokenizer.is_unknown(i):
-        toktype = gguf.TokenType.UNKNOWN
-    elif tokenizer.is_control(i):
-        toktype = gguf.TokenType.CONTROL
-    elif tokenizer.is_unused(i):
-        toktype = gguf.TokenType.UNUSED
-    elif tokenizer.is_byte(i):
-        toktype = gguf.TokenType.BYTE
-
-    toktypes.append(toktype)
-
-gguf_writer.add_tokenizer_model("llama")  # sentencepiece
-gguf_writer.add_token_list(tokens)
-gguf_writer.add_token_scores(scores)
-gguf_writer.add_token_types(toktypes)
-
-special_vocab = gguf.SpecialVocab(dir_model, load_merges=True)
-special_vocab.add_to_gguf(gguf_writer)
-
-print("gguf: get tensor metadata")
-
-model = AutoModelForCausalLM.from_pretrained(dir_model, config=config, low_cpu_mem_usage=True)
-#print(model)
-
-tensor_map = gguf.get_tensor_name_map(ARCH, block_count)
-
-list_vars = model.state_dict()
-for name in list_vars.keys():
-    print(name, list_vars[name].shape, list_vars[name].dtype)
-
-print(config)
-
-for name in list_vars.keys():
-    data = list_vars[name].squeeze().numpy()
-    print("Processing variable:", name, "with shape:", data.shape)
-
-    n_dims = len(data.shape)
-
-    # ftype == 0 -> float32, ftype == 1 -> float16
-    ftype_cur = 0
-    if ftype == 1 and name[-7:] == ".weight" and n_dims == 2:
-        print("  Converting to float16")
-        data = data.astype(np.float16)
-        ftype_cur = 1
-    elif ftype == 1 or data.dtype != np.float32:
-        print("  Converting to float32")
-        data = data.astype(np.float32)
-        ftype_cur = 0
-
-    # map tensor names
-    new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
-    if new_name is None:
-        print("Can not map tensor '" + name + "'")
-        sys.exit()
-
-    gguf_writer.add_tensor(new_name, data)
-
-
-print("gguf: write header")
-gguf_writer.write_header_to_file()
-print("gguf: write metadata")
-gguf_writer.write_kv_data_to_file()
-print("gguf: write tensors")
-gguf_writer.write_tensors_to_file()
-
-gguf_writer.close()
-
-print(f"gguf: model successfully exported to '{fname_out}'")
-print()

gpt4all-bindings/cli/README.md

@@ -40,5 +40,5 @@ directory, if necessary.
 If you have already saved a model beforehand, specify its path with the `-m`/`--model` argument,
 for example:
 ```shell
-python app.py repl --model /home/user/my-gpt4all-models/GPT4All-13B-snoozy.ggmlv3.q4_0.bin
+python app.py repl --model /home/user/my-gpt4all-models/gpt4all-13b-snoozy-q4_0.gguf
 ```

gpt4all-bindings/cli/app.py

@@ -1,3 +1,4 @@
+#!/usr/bin/env python3
 """GPT4All CLI
 
 The GPT4All CLI is a self-contained script based on the `gpt4all` and `typer` packages. It offers a
@@ -53,14 +54,18 @@ def repl(
     model: Annotated[
         str,
         typer.Option("--model", "-m", help="Model to use for chatbot"),
-    ] = "ggml-gpt4all-j-v1.3-groovy",
+    ] = "mistral-7b-instruct-v0.1.Q4_0.gguf",
     n_threads: Annotated[
         int,
         typer.Option("--n-threads", "-t", help="Number of threads to use for chatbot"),
     ] = None,
+    device: Annotated[
+        str,
+        typer.Option("--device", "-d", help="Device to use for chatbot, e.g. gpu, amd, nvidia, intel. Defaults to CPU."),
+    ] = None,
 ):
     """The CLI read-eval-print loop."""
-    gpt4all_instance = GPT4All(model)
+    gpt4all_instance = GPT4All(model, device=device)
 
     # if threads are passed, set them
     if n_threads is not None:
@@ -115,6 +120,7 @@ def _old_loop(gpt4all_instance):
             n_predict=200,
             top_k=40,
             top_p=0.9,
+            min_p=0.0,
             temp=0.9,
             n_batch=9,
             repeat_penalty=1.1,
@@ -151,6 +157,7 @@ def _new_loop(gpt4all_instance):
                 temp=0.9,
                 top_k=40,
                 top_p=0.9,
+                min_p=0.0,
                 repeat_penalty=1.1,
                 repeat_last_n=64,
                 n_batch=9,

gpt4all-bindings/csharp/.editorconfig

@@ -41,6 +41,8 @@ insert_final_newline = true
 
 # IDE0055: Fix formatting
 dotnet_diagnostic.IDE0055.severity = error
+dotnet_diagnostic.CS1573.severity = suggestion
+dotnet_diagnostic.CS1591.severity = suggestion
 
 # Sort using and Import directives with System.* appearing first
 dotnet_sort_system_directives_first = true
@@ -343,4 +345,4 @@ dotnet_diagnostic.IDE2004.severity = warning
 [src/{VisualStudio}/**/*.{cs,vb}]
 # CA1822: Make member static
 # There is a risk of accidentally breaking an internal API that partners rely on though IVT.
-dotnet_code_quality.CA1822.api_surface = private
+dotnet_code_quality.CA1822.api_surface = private

gpt4all-bindings/csharp/Directory.Build.props

@@ -5,7 +5,7 @@
         <Company></Company>
         <Copyright></Copyright>
         <NeutralLanguage>en-US</NeutralLanguage>
-        <Version>0.6.3-alpha</Version>
+        <Version>0.6.4-alpha</Version>
         <VersionSuffix>$(VersionSuffix)</VersionSuffix>
         <Version Condition=" '$(VersionSuffix)' != '' ">$(Version)$(VersionSuffix)</Version>
         <TreatWarningsAsErrors>true</TreatWarningsAsErrors>

gpt4all-bindings/csharp/Gpt4All.Samples/Gpt4All.Samples.csproj

@@ -2,9 +2,10 @@
 
     <PropertyGroup>
         <OutputType>Exe</OutputType>
-        <TargetFramework>net7.0</TargetFramework>
+        <TargetFramework>net8.0</TargetFramework>
         <ImplicitUsings>enable</ImplicitUsings>
         <Nullable>enable</Nullable>
+        <GenerateDocumentationFile>true</GenerateDocumentationFile>
     </PropertyGroup>
 
     <ItemGroup>

gpt4all-bindings/csharp/Gpt4All.Tests/Gpt4All.Tests.csproj

@@ -1,10 +1,11 @@
 <Project Sdk="Microsoft.NET.Sdk">
 
     <PropertyGroup>
-        <TargetFramework>net7.0</TargetFramework>
+        <TargetFramework>net8.0</TargetFramework>
         <Nullable>enable</Nullable>
 
         <IsPackable>false</IsPackable>
+        <GenerateDocumentationFile>true</GenerateDocumentationFile>
     </PropertyGroup>
 
     <ItemGroup>

gpt4all-bindings/csharp/Gpt4All/Bindings/ILLModel.cs

@@ -5,8 +5,6 @@
 /// </summary>
 public interface ILLModel : IDisposable
 {
-    ModelType ModelType { get; }
-
     ulong GetStateSizeBytes();
 
     int GetThreadCount();

gpt4all-bindings/csharp/Gpt4All/Bindings/LLModel.cs

@@ -42,16 +42,12 @@ public record ModelRecalculatingEventArgs(bool IsRecalculating);
 public class LLModel : ILLModel
 {
     protected readonly IntPtr _handle;
-    private readonly ModelType _modelType;
     private readonly ILogger _logger;
     private bool _disposed;
 
-    public ModelType ModelType => _modelType;
-
-    internal LLModel(IntPtr handle, ModelType modelType, ILogger? logger = null)
+    internal LLModel(IntPtr handle, ILogger? logger = null)
     {
         _handle = handle;
-        _modelType = modelType;
         _logger = logger ?? NullLogger.Instance;
     }
 
@@ -59,10 +55,9 @@ public class LLModel : ILLModel
     /// Create a new model from a pointer
     /// </summary>
     /// <param name="handle">Pointer to underlying model</param>
-    /// <param name="modelType">The model type</param>
-    public static LLModel Create(IntPtr handle, ModelType modelType, ILogger? logger = null)
+    public static LLModel Create(IntPtr handle, ILogger? logger = null)
     {
-        return new LLModel(handle, modelType, logger: logger);
+        return new LLModel(handle, logger: logger);
     }
 
     /// <summary>
@@ -188,7 +183,7 @@ public class LLModel : ILLModel
     /// <returns>true if the model was loaded successfully, false otherwise.</returns>
     public bool Load(string modelPath)
     {
-        return NativeMethods.llmodel_loadModel(_handle, modelPath);
+        return NativeMethods.llmodel_loadModel(_handle, modelPath, 2048, 100);
     }
 
     protected void Destroy()
@@ -204,12 +199,7 @@ public class LLModel : ILLModel
             // dispose managed state
         }
 
-        switch (_modelType)
-        {
-            default:
-                Destroy();
-                break;
-        }
+        Destroy();
 
         _disposed = true;
     }

gpt4all-bindings/csharp/Gpt4All/Bindings/LLPromptContext.cs

@@ -64,6 +64,15 @@ public unsafe class LLModelPromptContext
         set => _ctx.top_p = value;
     }
 
+    /// <summary>
+    /// min p sampling probability threshold
+    /// </summary>
+    public float MinP
+    {
+        get => _ctx.min_p;
+        set => _ctx.min_p = value;
+    }
+
     /// <summary>
     /// temperature to adjust model's output distribution
     /// </summary>

gpt4all-bindings/csharp/Gpt4All/Bindings/NativeMethods.cs

@@ -29,6 +29,8 @@ public unsafe partial struct llmodel_prompt_context
 
     public float top_p;
 
+    public float min_p;
+
     public float temp;
 
     [NativeTypeName("int32_t")]
@@ -70,7 +72,9 @@ internal static unsafe partial class NativeMethods
     [return: MarshalAs(UnmanagedType.I1)]
     public static extern bool llmodel_loadModel(
         [NativeTypeName("llmodel_model")] IntPtr model,
-        [NativeTypeName("const char *")][MarshalAs(UnmanagedType.LPUTF8Str)] string model_path);
+        [NativeTypeName("const char *")][MarshalAs(UnmanagedType.LPUTF8Str)] string model_path,
+        [NativeTypeName("int32_t")] int n_ctx,
+        [NativeTypeName("int32_t")] int ngl);
 
     [DllImport("libllmodel", CallingConvention = CallingConvention.Cdecl, ExactSpelling = true)]
 

gpt4all-bindings/csharp/Gpt4All/Extensions/LLPromptContextExtensions.cs

@@ -16,6 +16,7 @@ internal static class LLPromptContextExtensions
             n_predict = {ctx.n_predict}
             top_k = {ctx.top_k}
             top_p = {ctx.top_p}
+            min_p = {ctx.min_p}
             temp = {ctx.temp}
             n_batch = {ctx.n_batch}
             repeat_penalty = {ctx.repeat_penalty}

gpt4all-bindings/csharp/Gpt4All/Extensions/PredictRequestOptionsExtensions.cs

@@ -12,6 +12,7 @@ public static class PredictRequestOptionsExtensions
             TokensSize = opts.TokensSize,
             TopK = opts.TopK,
             TopP = opts.TopP,
+            MinP = opts.MinP,
             PastNum = opts.PastConversationTokensNum,
             RepeatPenalty = opts.RepeatPenalty,
             Temperature = opts.Temperature,

gpt4all-bindings/csharp/Gpt4All/Gpt4All.csproj

@@ -1,9 +1,10 @@
 <Project Sdk="Microsoft.NET.Sdk">
     <PropertyGroup>
-        <TargetFramework>net6.0</TargetFramework>
         <ImplicitUsings>enable</ImplicitUsings>
         <Nullable>enable</Nullable>
         <AllowUnsafeBlocks>true</AllowUnsafeBlocks>
+        <GenerateDocumentationFile>true</GenerateDocumentationFile>
+        <TargetFramework>net8.0</TargetFramework>
     </PropertyGroup>
     <ItemGroup>
         <!-- Windows -->

gpt4all-bindings/csharp/Gpt4All/Model/Gpt4AllModelFactory.cs

@@ -3,6 +3,7 @@ using Microsoft.Extensions.Logging.Abstractions;
 using Microsoft.Extensions.Logging;
 using Gpt4All.Bindings;
 using Gpt4All.LibraryLoader;
+using System.Runtime.InteropServices;
 
 namespace Gpt4All;
 
@@ -31,15 +32,18 @@ public class Gpt4AllModelFactory : IGpt4AllModelFactory
         }
     }
 
-    private IGpt4AllModel CreateModel(string modelPath)
+    private Gpt4All CreateModel(string modelPath)
     {
-        var modelType_ = ModelFileUtils.GetModelTypeFromModelFileHeader(modelPath);
-        _logger.LogInformation("Creating model path={ModelPath} type={ModelType}", modelPath, modelType_);
+        _logger.LogInformation("Creating model path={ModelPath}", modelPath);
         IntPtr error;
         var handle = NativeMethods.llmodel_model_create2(modelPath, "auto", out error);
+        if (error != IntPtr.Zero)
+        {
+            throw new Exception(Marshal.PtrToStringAnsi(error));
+        }
         _logger.LogDebug("Model created handle=0x{ModelHandle:X8}", handle);
         _logger.LogInformation("Model loading started");
-        var loadedSuccessfully = NativeMethods.llmodel_loadModel(handle, modelPath);
+        var loadedSuccessfully = NativeMethods.llmodel_loadModel(handle, modelPath, 2048, 100);
         _logger.LogInformation("Model loading completed success={ModelLoadSuccess}", loadedSuccessfully);
         if (!loadedSuccessfully)
         {
@@ -47,7 +51,7 @@ public class Gpt4AllModelFactory : IGpt4AllModelFactory
         }
 
         var logger = _loggerFactory.CreateLogger<LLModel>();
-        var underlyingModel = LLModel.Create(handle, modelType_, logger: logger);
+        var underlyingModel = LLModel.Create(handle, logger: logger);
 
         Debug.Assert(underlyingModel.IsLoaded());
 

gpt4all-bindings/csharp/Gpt4All/Model/ModelFileUtils.cs

@@ -1,24 +0,0 @@
-namespace Gpt4All;
-
-public static class ModelFileUtils
-{
-    private const uint GPTJ_MAGIC = 0x67676d6c;
-    private const uint LLAMA_MAGIC = 0x67676a74;
-    private const uint MPT_MAGIC = 0x67676d6d;
-
-    public static ModelType GetModelTypeFromModelFileHeader(string modelPath)
-    {
-        using var fileStream = new FileStream(modelPath, FileMode.Open);
-        using var binReader = new BinaryReader(fileStream);
-
-        var magic = binReader.ReadUInt32();
-
-        return magic switch
-        {
-            GPTJ_MAGIC => ModelType.GPTJ,
-            LLAMA_MAGIC => ModelType.LLAMA,
-            MPT_MAGIC => ModelType.MPT,
-            _ => throw new ArgumentOutOfRangeException($"Invalid model file. magic=0x{magic:X8}"),
-        };
-    }
-}

gpt4all-bindings/csharp/Gpt4All/Model/ModelOptions.cs

@@ -3,6 +3,4 @@
 public record ModelOptions
 {
     public int Threads { get; init; } = 4;
-
-    public ModelType ModelType { get; init; } = ModelType.GPTJ;
 }

gpt4all-bindings/csharp/Gpt4All/Model/ModelType.cs

@@ -1,11 +0,0 @@
-namespace Gpt4All;
-
-/// <summary>
-/// The supported model types
-/// </summary>
-public enum ModelType
-{
-    LLAMA = 0,
-    GPTJ,
-    MPT
-}

gpt4all-bindings/csharp/Gpt4All/Prediction/PredictRequestOptions.cs

@@ -16,6 +16,8 @@ public record PredictRequestOptions
 
     public float TopP { get; init; } = 0.9f;
 
+    public float MinP { get; init; } = 0.0f;
+
     public float Temperature { get; init; } = 0.1f;
 
     public int Batches { get; init; } = 8;

gpt4all-bindings/csharp/README.md

@@ -6,7 +6,10 @@ This package contains a set of C# bindings around the `llmodel` C-API.
 TBD
 
 ## Installation
-TBD NuGet
+
+Windows and Linux builds are available on NuGet: https://www.nuget.org/packages/Gpt4All
+
+macOS is WIP due to code signing issues, contributions are welcome.
 
 ## Project Structure
 ```

gpt4all-bindings/csharp/build_linux.sh

@@ -1,3 +1,4 @@
+#!/bin/sh
 mkdir -p runtimes
 rm -rf runtimes/linux-x64
 mkdir -p runtimes/linux-x64/native
@@ -7,4 +8,3 @@ cmake --build runtimes/linux-x64/build --parallel --config Release
 cp runtimes/linux-x64/build/libllmodel.so  runtimes/linux-x64/native/libllmodel.so
 cp runtimes/linux-x64/build/libgptj*.so  runtimes/linux-x64/native/
 cp runtimes/linux-x64/build/libllama*.so  runtimes/linux-x64/native/
-cp runtimes/linux-x64/build/libmpt*.so  runtimes/linux-x64/native/

gpt4all-bindings/golang/Makefile

@@ -139,7 +139,7 @@ $(info I CXX:      $(CXXV))
 $(info )
 
 llmodel.o:
-	mkdir buildllm
+	[ -e buildllm ] || mkdir buildllm
 	cd buildllm && cmake ../../../gpt4all-backend/ $(CMAKEFLAGS) && make
 	cd buildllm && cp -rf CMakeFiles/llmodel.dir/llmodel_c.cpp.o ../llmodel_c.o
 	cd buildllm && cp -rf CMakeFiles/llmodel.dir/llmodel.cpp.o ../llmodel.o
@@ -150,7 +150,7 @@ clean:
 	rm -rf buildllm
 	rm -rf example/main
 
-binding.o: 
+binding.o: binding.cpp binding.h
 	$(CXX) $(CXXFLAGS) binding.cpp -o binding.o -c $(LDFLAGS)
 
 libgpt4all.a: binding.o llmodel.o

gpt4all-bindings/golang/README.md

@@ -36,7 +36,7 @@ func main() {
 In order to use the bindings you will need to build `libgpt4all.a`:
 
 ```
-git clone https://github.com/nomic-ai/gpt4all
+git clone --recurse-submodules https://github.com/nomic-ai/gpt4all
 cd gpt4all/gpt4all-bindings/golang
 make libgpt4all.a
 ```

gpt4all-bindings/golang/binding.cpp

@@ -17,14 +17,13 @@
 
 void* load_model(const char *fname, int n_threads) {
     // load the model
-    llmodel_error new_error{};
+    const char *new_error;
     auto model = llmodel_model_create2(fname, "auto", &new_error);
-    if (model == nullptr ){
-        fprintf(stderr, "%s: error '%s'\n",
-                __func__, new_error.message);
+    if (model == nullptr) {
+        fprintf(stderr, "%s: error '%s'\n", __func__, new_error);
         return nullptr;
     }
-    if (!llmodel_loadModel(model, fname)) {
+    if (!llmodel_loadModel(model, fname, 2048, 100)) {
         llmodel_model_destroy(model);
         return nullptr;
     }
@@ -37,7 +36,7 @@ std::string res = "";
 void * mm;
 
 void model_prompt( const char *prompt, void *m, char* result, int repeat_last_n, float repeat_penalty, int n_ctx, int tokens, int top_k,
-                            float top_p, float temp, int n_batch,float ctx_erase)
+                            float top_p, float min_p, float temp, int n_batch,float ctx_erase)
 {
     llmodel_model* model = (llmodel_model*) m;
 
@@ -70,6 +69,7 @@ void model_prompt( const char *prompt, void *m, char* result, int repeat_last_n,
         .n_predict = 50,
         .top_k = 10,
         .top_p = 0.9,
+        .min_p = 0.0,
         .temp = 1.0,
         .n_batch = 1,
         .repeat_penalty = 1.2,
@@ -84,6 +84,7 @@ void model_prompt( const char *prompt, void *m, char* result, int repeat_last_n,
     prompt_context->top_k = top_k;
     prompt_context->context_erase = ctx_erase;
     prompt_context->top_p = top_p;
+    prompt_context->min_p = min_p;
     prompt_context->temp = temp;
     prompt_context->n_batch = n_batch;    
 

gpt4all-bindings/golang/binding.h

@@ -7,7 +7,7 @@ extern "C" {
 void* load_model(const char *fname, int n_threads);
 
 void model_prompt( const char *prompt, void *m, char* result, int repeat_last_n, float repeat_penalty, int n_ctx, int tokens, int top_k,
-                            float top_p, float temp, int n_batch,float ctx_erase);
+                            float top_p, float min_p, float temp, int n_batch,float ctx_erase);
 
 void free_model(void *state_ptr);
 
@@ -15,4 +15,4 @@ extern unsigned char getTokenCallback(void *, char *);
 
 #ifdef __cplusplus
 }
-#endif
+#endif

gpt4all-bindings/golang/gpt4all.go

@@ -7,7 +7,7 @@ package gpt4all
 // #cgo LDFLAGS: -lgpt4all -lm -lstdc++ -ldl
 // void* load_model(const char *fname, int n_threads);
 // void model_prompt( const char *prompt, void *m, char* result, int repeat_last_n, float repeat_penalty, int n_ctx, int tokens, int top_k,
-//                            float top_p, float temp, int n_batch,float ctx_erase);
+//                            float top_p, float min_p, float temp, int n_batch,float ctx_erase);
 // void free_model(void *state_ptr);
 // extern unsigned char getTokenCallback(void *, char *);
 // void llmodel_set_implementation_search_path(const char *path);
@@ -58,7 +58,7 @@ func (l *Model) Predict(text string, opts ...PredictOption) (string, error) {
 	out := make([]byte, po.Tokens)
 
 	C.model_prompt(input, l.state, (*C.char)(unsafe.Pointer(&out[0])), C.int(po.RepeatLastN), C.float(po.RepeatPenalty), C.int(po.ContextSize),
-		C.int(po.Tokens), C.int(po.TopK), C.float(po.TopP), C.float(po.Temperature), C.int(po.Batch), C.float(po.ContextErase))
+		C.int(po.Tokens), C.int(po.TopK), C.float(po.TopP), C.float(po.MinP), C.float(po.Temperature), C.int(po.Batch), C.float(po.ContextErase))
 
 	res := C.GoString((*C.char)(unsafe.Pointer(&out[0])))
 	res = strings.TrimPrefix(res, " ")

gpt4all-bindings/golang/options.go

@@ -2,7 +2,7 @@ package gpt4all
 
 type PredictOptions struct {
 	ContextSize, RepeatLastN, Tokens, TopK, Batch  int
-	TopP, Temperature, ContextErase, RepeatPenalty float64
+	TopP, MinP, Temperature, ContextErase, RepeatPenalty float64
 }
 
 type PredictOption func(p *PredictOptions)
@@ -11,6 +11,7 @@ var DefaultOptions PredictOptions = PredictOptions{
 	Tokens:        200,
 	TopK:          10,
 	TopP:          0.90,
+        MinP:          0.0,
 	Temperature:   0.96,
 	Batch:         1,
 	ContextErase:  0.55,
@@ -50,6 +51,13 @@ func SetTopP(topp float64) PredictOption {
 	}
 }
 
+// SetMinP sets the value for min p sampling
+func SetMinP(minp float64) PredictOption {
+	return func(p *PredictOptions) {
+		p.MinP = minp
+	}
+}
+
 // SetRepeatPenalty sets the repeat penalty.
 func SetRepeatPenalty(ce float64) PredictOption {
 	return func(p *PredictOptions) {

gpt4all-bindings/java/README.md

@@ -22,7 +22,7 @@ implementation 'com.hexadevlabs:gpt4all-java-binding:1.1.5'
 
 To add the library dependency for another build system see [Maven Central Java bindings](https://central.sonatype.com/artifact/com.hexadevlabs/gpt4all-java-binding/).
 
-To download model binary weights file use a URL such as [`https://gpt4all.io/models/ggml-gpt4all-j-v1.3-groovy.bin`](https://gpt4all.io/models/ggml-gpt4all-j-v1.3-groovy.bin).
+To download model binary weights file use a URL such as [`https://gpt4all.io/models/gguf/gpt4all-13b-snoozy-q4_0.gguf`](https://gpt4all.io/models/gguf/gpt4all-13b-snoozy-q4_0.gguf).
 
 For information about other models available see the [model file list](https://github.com/nomic-ai/gpt4all/tree/main/gpt4all-chat#manual-download-of-models).
 
@@ -123,4 +123,4 @@ If this is the case you can easily download and install the latest x64 Microsoft
    - Falcon model support included.
 4. Version **1.1.5**:
    - Add a check for model file readability before loading model.
-   
+   

gpt4all-bindings/java/src/main/java/com/hexadevlabs/gpt4all/LLModel.java

@@ -1,6 +1,7 @@
 package com.hexadevlabs.gpt4all;
 
 import jnr.ffi.Pointer;
+import jnr.ffi.byref.PointerByReference;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 
@@ -31,6 +32,7 @@ public  class LLModel implements AutoCloseable {
             n_predict.set(128);
             top_k.set(40);
             top_p.set(0.95);
+            min_p.set(0.0);
             temp.set(0.28);
             n_batch.set(8);
             repeat_penalty.set(1.1);
@@ -70,6 +72,11 @@ public  class LLModel implements AutoCloseable {
                 return this;
             }
 
+            public Builder withMinP(float min_p) {
+                configToBuild.min_p.set(min_p);
+                return this;
+            }
+
             public Builder withTemp(float temp) {
                 configToBuild.temp.set(temp);
                 return this;
@@ -176,7 +183,7 @@ public  class LLModel implements AutoCloseable {
         modelName = modelPath.getFileName().toString();
         String modelPathAbs = modelPath.toAbsolutePath().toString();
 
-        LLModelLibrary.LLModelError error = new LLModelLibrary.LLModelError(jnr.ffi.Runtime.getSystemRuntime());
+        PointerByReference error = new PointerByReference();
 
         // Check if model file exists
         if(!Files.exists(modelPath)){
@@ -192,9 +199,9 @@ public  class LLModel implements AutoCloseable {
         model = library.llmodel_model_create2(modelPathAbs, "auto", error);
 
         if(model == null) {
-            throw new IllegalStateException("Could not load, gpt4all backend returned error: " + error.message);
+            throw new IllegalStateException("Could not load, gpt4all backend returned error: " + error.getValue().getString(0));
         }
-        library.llmodel_loadModel(model, modelPathAbs);
+        library.llmodel_loadModel(model, modelPathAbs, 2048, 100);
 
         if(!library.llmodel_isModelLoaded(model)){
             throw new IllegalStateException("The model " + modelName + " could not be loaded");
@@ -631,4 +638,4 @@ public  class LLModel implements AutoCloseable {
         library.llmodel_model_destroy(model);
     }
 
-}
+}

gpt4all-bindings/java/src/main/java/com/hexadevlabs/gpt4all/LLModelLibrary.java

@@ -1,6 +1,7 @@
 package com.hexadevlabs.gpt4all;
 
 import jnr.ffi.Pointer;
+import jnr.ffi.byref.PointerByReference;
 import jnr.ffi.Struct;
 import jnr.ffi.annotations.Delegate;
 import jnr.ffi.annotations.Encoding;
@@ -47,6 +48,7 @@ public interface LLModelLibrary {
         public final int32_t n_predict = new int32_t();
         public final int32_t top_k = new int32_t();
         public final Float top_p = new Float();
+        public final Float min_p = new Float();
         public final Float temp = new Float();
         public final int32_t n_batch = new int32_t();
         public final Float repeat_penalty = new Float();
@@ -58,9 +60,9 @@ public interface LLModelLibrary {
         }
     }
 
-    Pointer llmodel_model_create2(String model_path, String build_variant, @Out LLModelError llmodel_error);
+    Pointer llmodel_model_create2(String model_path, String build_variant, PointerByReference error);
     void llmodel_model_destroy(Pointer model);
-    boolean llmodel_loadModel(Pointer model, String model_path);
+    boolean llmodel_loadModel(Pointer model, String model_path, int n_ctx, int ngl);
     boolean llmodel_isModelLoaded(Pointer model);
     @u_int64_t long llmodel_get_state_size(Pointer model);
     @u_int64_t long llmodel_save_state_data(Pointer model, Pointer dest);

gpt4all-bindings/python/README.md

@@ -9,11 +9,17 @@ https://docs.gpt4all.io/gpt4all_python.html
 
 ## Installation
 
+The easiest way to install the Python bindings for GPT4All is to use pip:
+
 ```
 pip install gpt4all
 ```
 
-## Local Build Instructions
+This will download the latest version of the `gpt4all` package from PyPI.
+
+## Local Build
+
+As an alternative to downloading via pip, you may build the Python bindings from source.
 
 ### Prerequisites
 
@@ -23,25 +29,32 @@ macOS users do not need Vulkan, as GPT4All will use Metal instead.
 
 ### Building the python bindings
 
-**NOTE**: If you are doing this on a Windows machine, you must build the GPT4All backend using [MinGW64](https://www.mingw-w64.org/) compiler.
-
-1. Setup `llmodel`
-
+1. Clone GPT4All and change directory:
 ```
 git clone --recurse-submodules https://github.com/nomic-ai/gpt4all.git
-cd gpt4all/gpt4all-backend/
-mkdir build
-cd build
-cmake ..
-cmake --build . --parallel  # optionally append: --config Release
+cd gpt4all/gpt4all-backend
 ```
-Confirm that `libllmodel.*` exists in `gpt4all-backend/build`.
 
-2. Setup Python package
+2. Build the backend.
 
+If you are using Windows and have Visual Studio installed:
+```
+cmake -B build
+cmake --build build --parallel --config RelWithDebInfo
+```
+
+For all other platforms:
+```
+cmake -B build -DCMAKE_BUILD_TYPE=RelWithDebInfo
+cmake --build build --parallel
+```
+
+`RelWithDebInfo` is a good default, but you can also use `Release` or `Debug` depending on the situation.
+
+2. Install the Python package:
 ```
 cd ../../gpt4all-bindings/python
-pip3 install -e .
+pip install -e .
 ```
 
 ## Usage
@@ -50,7 +63,7 @@ Test it out! In a Python script or console:
 
 ```python
 from gpt4all import GPT4All
-model = GPT4All("orca-mini-3b.ggmlv3.q4_0.bin")
+model = GPT4All("orca-mini-3b-gguf2-q4_0.gguf")
 output = model.generate("The capital of France is ", max_tokens=3)
 print(output)
 ```
@@ -59,7 +72,7 @@ print(output)
 GPU Usage
 ```python
 from gpt4all import GPT4All
-model = GPT4All("orca-mini-3b.ggmlv3.q4_0.bin", device='gpu') # device='amd', device='intel'
+model = GPT4All("orca-mini-3b-gguf2-q4_0.gguf", device='gpu') # device='amd', device='intel'
 output = model.generate("The capital of France is ", max_tokens=3)
 print(output)
 ```

gpt4all-bindings/python/docs/gpt4all_chat.md

@@ -5,48 +5,46 @@ The [GPT4All Chat Client](https://gpt4all.io) lets you easily interact with any
 It is optimized to run 7-13B parameter LLMs on the CPU's of any computer running OSX/Windows/Linux.
 
 ## Running LLMs on CPU
-The GPT4All Chat UI supports models from all newer versions of `GGML`, `llama.cpp` including the `LLaMA`, `MPT`, `replit`,  `GPT-J` and `falcon` architectures
+The GPT4All Chat UI supports models from all newer versions of `llama.cpp` with `GGUF` models including the `Mistral`, `LLaMA2`, `LLaMA`, `OpenLLaMa`, `Falcon`, `MPT`, `Replit`, `Starcoder`, and `Bert` architectures
 
-GPT4All maintains an official list of recommended models located in [models2.json](https://github.com/nomic-ai/gpt4all/blob/main/gpt4all-chat/metadata/models2.json). You can pull request new models to it and if accepted they will show up in the official download dialog.
+GPT4All maintains an official list of recommended models located in [models3.json](https://github.com/nomic-ai/gpt4all/blob/main/gpt4all-chat/metadata/models3.json). You can pull request new models to it and if accepted they will show up in the official download dialog.
 
-#### Sideloading any GGML model
+#### Sideloading any GGUF model
 If a model is compatible with the gpt4all-backend, you can sideload it into GPT4All Chat by:
 
-1. Downloading your model in GGML format. It should be a 3-8 GB file similar to the ones [here](https://huggingface.co/TheBloke/Samantha-7B-GGML/tree/main).
-2. Identifying your GPT4All model downloads folder. This is the path listed at the bottom of the downloads dialog(Three lines in top left>Downloads).
-3. Placing your downloaded model inside the GPT4All's model downloads folder.
+1. Downloading your model in GGUF format. It should be a 3-8 GB file similar to the ones [here](https://huggingface.co/TheBloke/Orca-2-7B-GGUF/tree/main).
+2. Identifying your GPT4All model downloads folder. This is the path listed at the bottom of the downloads dialog.
+3. Placing your downloaded model inside GPT4All's model downloads folder.
 4. Restarting your GPT4ALL app. Your model should appear in the model selection list.
 
 ## Plugins
 GPT4All Chat Plugins allow you to expand the capabilities of Local LLMs.
 
-### LocalDocs Beta Plugin (Chat With Your Data)
-LocalDocs is a GPT4All plugin that allows you to chat with your local files and data.
+### LocalDocs Plugin (Chat With Your Data)
+LocalDocs is a GPT4All feature that allows you to chat with your local files and data.
 It allows you to utilize powerful local LLMs to chat with private data without any data leaving your computer or server.
-When using LocalDocs, your LLM will cite the sources that most likely contributed to a given output. Note, even an LLM equipped with LocalDocs can hallucinate. If the LocalDocs plugin decides to utilize your documents to help answer a prompt, you will see references appear below the response.
+When using LocalDocs, your LLM will cite the sources that most likely contributed to a given output. Note, even an LLM equipped with LocalDocs can hallucinate. The LocalDocs plugin will utilize your documents to help answer prompts and you will see references appear below the response.
 
 <p align="center">
-  <img width="70%" src="https://github.com/nomic-ai/gpt4all/assets/13879686/f70f40b4-9684-46d8-b388-ca186f63d13e">
-</p>
-<p align="center">
-GPT4All-Snoozy with LocalDocs. Try GPT4All-Groovy for a faster experience!
+  <img width="70%" src="https://github.com/nomic-ai/gpt4all/assets/10168/fe5dd3c0-b3cc-4701-98d3-0280dfbcf26f">
 </p>
 
 #### Enabling LocalDocs
 1. Install the latest version of GPT4All Chat from [GPT4All Website](https://gpt4all.io).
 2. Go to `Settings > LocalDocs tab`.
-3. Configure a collection (folder) on your computer that contains the files your LLM should have access to. You can alter the contents of the folder/directory at anytime. As you
+3. Download the SBert model
+4. Configure a collection (folder) on your computer that contains the files your LLM should have access to. You can alter the contents of the folder/directory at anytime. As you
 add more files to your collection, your LLM will dynamically be able to access them.
-4. Spin up a chat session with any LLM (including external ones like ChatGPT but warning data will leave your machine!)
-5. At the top right, click the database icon and select which collection you want your LLM to know about during your chat session.
+5. Spin up a chat session with any LLM (including external ones like ChatGPT but warning data will leave your machine!)
+6. At the top right, click the database icon and select which collection you want your LLM to know about during your chat session.
+7. You can begin searching with your localdocs even before the collection has completed indexing, but note the search will not include those parts of the collection yet to be indexed.
 
 #### LocalDocs Capabilities
-LocalDocs allows your LLM to have context about the contents of your documentation collection. Not all prompts/question will utilize your document
-collection for context. If LocalDocs was used in your LLMs response, you will see references to the document snippets that LocalDocs used.
+LocalDocs allows your LLM to have context about the contents of your documentation collection.
 
 LocalDocs **can**:
 
-- Query your documents based upon your prompt / question. If your documents contain answers that may help answer your question/prompt LocalDocs will try to utilize snippets of your documents to provide context.
+- Query your documents based upon your prompt / question. Your documents will be searched for snippets that can be used to provide context for an answer. The most relevant snippets will be inserted into your prompts context, but it will be up to the underlying model to decide how best to use the provided context.
 
 LocalDocs **cannot**:
 
@@ -62,32 +60,17 @@ The general technique this plugin uses is called [Retrieval Augmented Generation
 
 These document chunks help your LLM respond to queries with knowledge about the contents of your data.
 The number of chunks and the size of each chunk can be configured in the LocalDocs plugin settings tab.
-For indexing speed purposes, LocalDocs uses pre-deep-learning n-gram and TF-IDF based retrieval when deciding
-what document chunks your LLM should use as context. You'll find its of comparable quality
-with embedding based retrieval approaches but magnitudes faster to ingest data.
 
-LocalDocs supports the following file types:
-```json
-["txt", "doc", "docx", "pdf", "rtf", "odt", "html", "htm", "xls", "xlsx", "csv", "ods", "ppt", "pptx", "odp", "xml", "json", "log", "md", "org", "tex", "asc", "wks",
-"wpd", "wps", "wri", "xhtml", "xht", "xslt", "yaml", "yml", "dtd", "sgml", "tsv", "strings", "resx",
-"plist", "properties", "ini", "config", "bat", "sh", "ps1", "cmd", "awk", "sed", "vbs", "ics", "mht",
-"mhtml", "epub", "djvu", "azw", "azw3", "mobi", "fb2", "prc", "lit", "lrf", "tcr", "pdb", "oxps",
-"xps", "pages", "numbers", "key", "keynote", "abw", "zabw", "123", "wk1", "wk3", "wk4", "wk5", "wq1",
-"wq2", "xlw", "xlr", "dif", "slk", "sylk", "wb1", "wb2", "wb3", "qpw", "wdb", "wks", "wku", "wr1",
-"wrk", "xlk", "xlt", "xltm", "xltx", "xlsm", "xla", "xlam", "xll", "xld", "xlv", "xlw", "xlc", "xlm",
-"xlt", "xln"]
-```
+LocalDocs currently supports plain text files (`.txt`, `.md`, and `.rst`) and PDF files (`.pdf`).
 
 #### Troubleshooting and FAQ
 *My LocalDocs plugin isn't using my documents*
 
 - Make sure LocalDocs is enabled for your chat session (the DB icon on the top-right should have a border)
-- Try to modify your prompt to be more specific and use terminology that is in your document. This will increase the likelihood that LocalDocs matches document snippets for your question.
 - If your document collection is large, wait 1-2 minutes for it to finish indexing.
 
 
 #### LocalDocs Roadmap
-- Embedding based semantic search for retrieval. 
 - Customize model fine-tuned with retrieval in the loop.
 - Plugin compatibility with chat client server mode.
 

gpt4all-bindings/python/docs/gpt4all_cli.md

@@ -5,7 +5,7 @@ The GPT4All command-line interface (CLI) is a Python script which is built on to
 package. The source code, README, and local build instructions can be found
 [here][repo-bindings-cli].
 
-[docs-bindings-python]: gpt4all_python.html
+[docs-bindings-python]: gpt4all_python.md
 [repo-bindings-python]: https://github.com/nomic-ai/gpt4all/tree/main/gpt4all-bindings/python
 [repo-bindings-cli]: https://github.com/nomic-ai/gpt4all/tree/main/gpt4all-bindings/cli
 [typer]: https://typer.tiangolo.com/
@@ -166,7 +166,7 @@ If you want to use a different model, you can do so with the `-m`/`--model` para
 model file name is provided, it will again check in `.cache/gpt4all/` and might start downloading.
 If instead given a path to an existing model, the command could for example look like this:
 ```shell
-python app.py repl --model /home/user/my-gpt4all-models/GPT4All-13B-snoozy.ggmlv3.q4_0.bin
+python app.py repl --model /home/user/my-gpt4all-models/gpt4all-13b-snoozy-q4_0.gguf
 ```
 
 When you're done and want to end a session, simply type `/exit`.

gpt4all-bindings/python/docs/gpt4all_faq.md

@@ -61,12 +61,12 @@ or `allowDownload=true` (default), a model is automatically downloaded into `.ca
 unless it already exists.
 
 In case of connection issues or errors during the download, you might want to manually verify the model file's MD5
-checksum by comparing it with the one listed in [models2.json].
+checksum by comparing it with the one listed in [models3.json].
 
 As an alternative to the basic downloader built into the bindings, you can choose to download from the 
 <https://gpt4all.io/> website instead. Scroll down to 'Model Explorer' and pick your preferred model.
 
-[models2.json]: https://github.com/nomic-ai/gpt4all/blob/main/gpt4all-chat/metadata/models2.json
+[models3.json]: https://github.com/nomic-ai/gpt4all/blob/main/gpt4all-chat/metadata/models3.json
 
 #### I need the chat GUI and bindings to behave the same
 
@@ -93,7 +93,7 @@ The chat GUI and bindings are based on the same backend. You can make them behav
 - Next you'll have to compare the templates, adjusting them as necessary, based on how you're using the bindings.
     - Specifically, in Python:
         - With simple `generate()` calls, the input has to be surrounded with system and prompt templates.
-        - When using a chat session, it depends on whether the bindings are allowed to download [models2.json]. If yes,
+        - When using a chat session, it depends on whether the bindings are allowed to download [models3.json]. If yes,
           and in the chat GUI the default templates are used, it'll be handled automatically. If no, use
           `chat_session()` template parameters to customize them.
 

gpt4all-bindings/python/docs/gpt4all_modal.md

@@ -1,34 +0,0 @@
-# GPT4All with Modal Labs
-
-You can easily query any GPT4All model on [Modal Labs](https://modal.com/) infrastructure!
-## Example
-
-```python
-import modal
-
-def download_model():
-    import gpt4all
-    #you can use any model from https://gpt4all.io/models/models2.json
-    return gpt4all.GPT4All("ggml-gpt4all-j-v1.3-groovy.bin")
-
-image=modal.Image.debian_slim().pip_install("gpt4all").run_function(download_model)
-stub = modal.Stub("gpt4all", image=image)
-@stub.cls(keep_warm=1)
-class GPT4All:
-    def __enter__(self):
-        print("Downloading model")
-        self.gptj = download_model()
-        print("Loaded model")
-
-    @modal.method()
-    def generate(self):
-        messages = [{"role": "user", "content": "Name 3 colors"}]
-        completion = self.gptj.chat_completion(messages)
-        print(f"Completion: {completion}")
-
-@stub.local_entrypoint()
-def main():
-    model = GPT4All()
-    for i in range(10):
-        model.generate.call()
-```

gpt4all-bindings/python/docs/gpt4all_nodejs.md

@@ -1,11 +1,14 @@
 # GPT4All Node.js API
 
+Native Node.js LLM bindings for all.
+
 ```sh
-yarn add gpt4all@alpha
+yarn add gpt4all@latest
 
-npm install gpt4all@alpha
+npm install gpt4all@latest
+
+pnpm install gpt4all@latest
 
-pnpm install gpt4all@alpha
 ```
 
 The original [GPT4All typescript bindings](https://github.com/nomic-ai/gpt4all-ts) are now out of date.
@@ -15,12 +18,12 @@ The original [GPT4All typescript bindings](https://github.com/nomic-ai/gpt4all-t
 *   Everything should work out the box.
 *   See [API Reference](#api-reference)
 
-### Chat Completion (alpha)
+### Chat Completion
 
 ```js
 import { createCompletion, loadModel } from '../src/gpt4all.js'
 
-const model = await loadModel('ggml-vicuna-7b-1.1-q4_2', { verbose: true });
+const model = await loadModel('mistral-7b-openorca.Q4_0.gguf', { verbose: true });
 
 const response = await createCompletion(model, [
     { role : 'system', content: 'You are meant to be annoying and unhelpful.'  },
@@ -29,7 +32,7 @@ const response = await createCompletion(model, [
 
 ```
 
-### Embedding (alpha)
+### Embedding
 
 ```js
 import { createEmbedding, loadModel } from '../src/gpt4all.js'
@@ -82,8 +85,6 @@ yarn
 git submodule update --init --depth 1 --recursive
 ```
 
-**AS OF NEW BACKEND** to build the backend,
-
 ```sh
 yarn build:backend
 ```
@@ -152,13 +153,17 @@ This package is in active development, and breaking changes may happen until the
 
 ##### Table of Contents
 
-*   [ModelType](#modeltype)
 *   [ModelFile](#modelfile)
     *   [gptj](#gptj)
     *   [llama](#llama)
     *   [mpt](#mpt)
     *   [replit](#replit)
 *   [type](#type)
+*   [TokenCallback](#tokencallback)
+*   [InferenceModel](#inferencemodel)
+    *   [dispose](#dispose)
+*   [EmbeddingModel](#embeddingmodel)
+    *   [dispose](#dispose-1)
 *   [LLModel](#llmodel)
     *   [constructor](#constructor)
         *   [Parameters](#parameters)
@@ -176,12 +181,21 @@ This package is in active development, and breaking changes may happen until the
     *   [setLibraryPath](#setlibrarypath)
         *   [Parameters](#parameters-4)
     *   [getLibraryPath](#getlibrarypath)
+    *   [initGpuByString](#initgpubystring)
+        *   [Parameters](#parameters-5)
+    *   [hasGpuDevice](#hasgpudevice)
+    *   [listGpu](#listgpu)
+        *   [Parameters](#parameters-6)
+    *   [dispose](#dispose-2)
+*   [GpuDevice](#gpudevice)
+    *   [type](#type-2)
+*   [LoadModelOptions](#loadmodeloptions)
 *   [loadModel](#loadmodel)
-    *   [Parameters](#parameters-5)
-*   [createCompletion](#createcompletion)
-    *   [Parameters](#parameters-6)
-*   [createEmbedding](#createembedding)
     *   [Parameters](#parameters-7)
+*   [createCompletion](#createcompletion)
+    *   [Parameters](#parameters-8)
+*   [createEmbedding](#createembedding)
+    *   [Parameters](#parameters-9)
 *   [CompletionOptions](#completionoptions)
     *   [verbose](#verbose)
     *   [systemPromptTemplate](#systemprompttemplate)
@@ -213,15 +227,15 @@ This package is in active development, and breaking changes may happen until the
     *   [repeatPenalty](#repeatpenalty)
     *   [repeatLastN](#repeatlastn)
     *   [contextErase](#contexterase)
-*   [createTokenStream](#createtokenstream)
-    *   [Parameters](#parameters-8)
+*   [generateTokens](#generatetokens)
+    *   [Parameters](#parameters-10)
 *   [DEFAULT\_DIRECTORY](#default_directory)
 *   [DEFAULT\_LIBRARIES\_DIRECTORY](#default_libraries_directory)
 *   [DEFAULT\_MODEL\_CONFIG](#default_model_config)
-*   [DEFAULT\_PROMT\_CONTEXT](#default_promt_context)
+*   [DEFAULT\_PROMPT\_CONTEXT](#default_prompt_context)
 *   [DEFAULT\_MODEL\_LIST\_URL](#default_model_list_url)
 *   [downloadModel](#downloadmodel)
-    *   [Parameters](#parameters-9)
+    *   [Parameters](#parameters-11)
     *   [Examples](#examples)
 *   [DownloadModelOptions](#downloadmodeloptions)
     *   [modelPath](#modelpath)
@@ -232,16 +246,10 @@ This package is in active development, and breaking changes may happen until the
     *   [cancel](#cancel)
     *   [promise](#promise)
 
-#### ModelType
-
-Type of the model
-
-Type: (`"gptj"` | `"llama"` | `"mpt"` | `"replit"`)
-
 #### ModelFile
 
 Full list of models available
-@deprecated These model names are outdated and this type will not be maintained, please use a string literal instead
+DEPRECATED!! These model names are outdated and this type will not be maintained, please use a string literal instead
 
 ##### gptj
 
@@ -271,7 +279,33 @@ Type: `"ggml-replit-code-v1-3b.bin"`
 
 Model architecture. This argument currently does not have any functionality and is just used as descriptive identifier for user.
 
-Type: [ModelType](#modeltype)
+Type: ModelType
+
+#### TokenCallback
+
+Callback for controlling token generation
+
+Type: function (tokenId: [number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number), token: [string](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String), total: [string](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String)): [boolean](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Boolean)
+
+#### InferenceModel
+
+InferenceModel represents an LLM which can make chat predictions, similar to GPT transformers.
+
+##### dispose
+
+delete and cleanup the native model
+
+Returns **void** 
+
+#### EmbeddingModel
+
+EmbeddingModel represents an LLM which can create embeddings, which are float arrays
+
+##### dispose
+
+delete and cleanup the native model
+
+Returns **void** 
 
 #### LLModel
 
@@ -294,7 +328,7 @@ Initialize a new LLModel.
 
 either 'gpt', mpt', or 'llama' or undefined
 
-Returns **([ModelType](#modeltype) | [undefined](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/undefined))** 
+Returns **(ModelType | [undefined](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/undefined))** 
 
 ##### name
 
@@ -336,9 +370,9 @@ Use the prompt function exported for a value
 
 *   `q` **[string](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String)** The prompt input.
 *   `params` **Partial<[LLModelPromptContext](#llmodelpromptcontext)>** Optional parameters for the prompt context.
-*   `callback` **function (res: [string](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String)): void**&#x20;
+*   `callback` **[TokenCallback](#tokencallback)?** optional callback to control token generation.
 
-Returns **void** The result of the model prompt.
+Returns **[Promise](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Promise)<[string](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String)>** The result of the model prompt.
 
 ##### embed
 
@@ -376,6 +410,58 @@ Where to get the pluggable backend libraries
 
 Returns **[string](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String)**&#x20;
 
+##### initGpuByString
+
+Initiate a GPU by a string identifier.
+
+###### Parameters
+
+*   `memory_required` **[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)** Should be in the range size\_t or will throw
+*   `device_name` **[string](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String)** 'amd' | 'nvidia' | 'intel' | 'gpu' | gpu name.
+    read LoadModelOptions.device for more information
+
+Returns **[boolean](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Boolean)**&#x20;
+
+##### hasGpuDevice
+
+From C documentation
+
+Returns **[boolean](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Boolean)** True if a GPU device is successfully initialized, false otherwise.
+
+##### listGpu
+
+GPUs that are usable for this LLModel
+
+###### Parameters
+
+*   `nCtx` **[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)** Maximum size of context window
+
+<!---->
+
+*   Throws **any** if hasGpuDevice returns false (i think)
+
+Returns **[Array](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Array)<[GpuDevice](#gpudevice)>**&#x20;
+
+##### dispose
+
+delete and cleanup the native model
+
+Returns **void**&#x20;
+
+#### GpuDevice
+
+an object that contains gpu data on this machine.
+
+##### type
+
+same as VkPhysicalDeviceType
+
+Type: [number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)
+
+#### LoadModelOptions
+
+Options that configure a model's behavior.
+
 #### loadModel
 
 Loads a machine learning model with the specified name. The defacto way to create a model.
@@ -384,9 +470,9 @@ By default this will download a model from the official GPT4ALL website, if a mo
 ##### Parameters
 
 *   `modelName` **[string](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String)** The name of the model to load.
-*   `options` **(LoadModelOptions | [undefined](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/undefined))?** (Optional) Additional options for loading the model.
+*   `options` **([LoadModelOptions](#loadmodeloptions) | [undefined](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/undefined))?** (Optional) Additional options for loading the model.
 
-Returns **[Promise](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Promise)<(InferenceModel | EmbeddingModel)>** A promise that resolves to an instance of the loaded LLModel.
+Returns **[Promise](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Promise)<([InferenceModel](#inferencemodel) | [EmbeddingModel](#embeddingmodel))>** A promise that resolves to an instance of the loaded LLModel.
 
 #### createCompletion
 
@@ -394,7 +480,7 @@ The nodejs equivalent to python binding's chat\_completion
 
 ##### Parameters
 
-*   `model` **InferenceModel** The language model object.
+*   `model` **[InferenceModel](#inferencemodel)** The language model object.
 *   `messages` **[Array](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Array)<[PromptMessage](#promptmessage)>** The array of messages for the conversation.
 *   `options` **[CompletionOptions](#completionoptions)** The options for creating the completion.
 
@@ -407,7 +493,7 @@ meow
 
 ##### Parameters
 
-*   `model` **EmbeddingModel** The language model object.
+*   `model` **[EmbeddingModel](#embeddingmodel)** The language model object.
 *   `text` **[string](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String)** text to embed
 
 Returns **[Float32Array](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Float32Array)** The completion result.
@@ -618,17 +704,18 @@ The percentage of context to erase if the context window is exceeded.
 
 Type: [number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)
 
-#### createTokenStream
+#### generateTokens
 
-TODO: Help wanted to implement this
+Creates an async generator of tokens
 
 ##### Parameters
 
-*   `llmodel` **[LLModel](#llmodel)**&#x20;
-*   `messages` **[Array](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Array)<[PromptMessage](#promptmessage)>**&#x20;
-*   `options` **[CompletionOptions](#completionoptions)**&#x20;
+*   `llmodel` **[InferenceModel](#inferencemodel)** The language model object.
+*   `messages` **[Array](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Array)<[PromptMessage](#promptmessage)>** The array of messages for the conversation.
+*   `options` **[CompletionOptions](#completionoptions)** The options for creating the completion.
+*   `callback` **[TokenCallback](#tokencallback)** optional callback to control token generation.
 
-Returns **function (ll: [LLModel](#llmodel)): AsyncGenerator<[string](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String)>**&#x20;
+Returns **AsyncGenerator<[string](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String)>** The stream of generated tokens
 
 #### DEFAULT\_DIRECTORY
 
@@ -652,7 +739,7 @@ Default model configuration.
 
 Type: ModelConfig
 
-#### DEFAULT\_PROMT\_CONTEXT
+#### DEFAULT\_PROMPT\_CONTEXT
 
 Default prompt context.
 
@@ -705,7 +792,7 @@ Type: [boolean](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Glob
 
 ##### url
 
-Remote download url. Defaults to `https://gpt4all.io/models/<modelName>`
+Remote download url. Defaults to `https://gpt4all.io/models/gguf/<modelName>`
 
 Type: [string](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String)
 

gpt4all-bindings/python/docs/gpt4all_python.md

@@ -8,34 +8,26 @@ The source code and local build instructions can be found [here](https://github.
 pip install gpt4all
 ```
 
-=== "GPT4All Example"
-    ``` py
-    from gpt4all import GPT4All
-    model = GPT4All("orca-mini-3b.ggmlv3.q4_0.bin")
-    output = model.generate("The capital of France is ", max_tokens=3)
-    print(output)
-    ```
-=== "Output"
-    ```
-    1. Paris
-    ```
+``` py
+from gpt4all import GPT4All
+model = GPT4All("orca-mini-3b-gguf2-q4_0.gguf")
+```
 
 This will:
 
 - Instantiate `GPT4All`,  which is the primary public API to your large language model (LLM).
 - Automatically download the given model to `~/.cache/gpt4all/` if not already present.
-- Through `model.generate(...)` the model starts working on a response. There are various ways to
-  steer that process. Here, `max_tokens` sets an upper limit, i.e. a hard cut-off point to the output.
+
+Read further to see how to chat with this model.
 
 
 ### Chatting with GPT4All
-Local LLMs can be optimized for chat conversations by reusing previous computational history.
-
-Use the GPT4All `chat_session` context manager to hold chat conversations with the model.
+To start chatting with a local LLM, you will need to start a chat session. Within a chat session, the model will be
+prompted with the appropriate template, and history will be preserved between successive calls to `generate()`.
 
 === "GPT4All Example"
     ``` py
-    model = GPT4All(model_name='orca-mini-3b.ggmlv3.q4_0.bin')
+    model = GPT4All(model_name='orca-mini-3b-gguf2-q4_0.gguf')
     with model.chat_session():
         response1 = model.generate(prompt='hello', temp=0)
         response2 = model.generate(prompt='write me a short poem', temp=0)
@@ -72,15 +64,19 @@ Use the GPT4All `chat_session` context manager to hold chat conversations with t
     ]
     ```
 
-When using GPT4All models in the `chat_session` context:
+When using GPT4All models in the `chat_session()` context:
 
 - Consecutive chat exchanges are taken into account and not discarded until the session ends; as long as the model has capacity.
-- Internal K/V caches are preserved from previous conversation history, speeding up inference.
-- The model is given a system and prompt template which make it chatty. Depending on `allow_download=True` (default),
-  it will obtain the latest version of [models2.json] from the repository, which contains specifically tailored templates
-  for models. Conversely, if it is not allowed to download, it falls back to default templates instead.
+- A system prompt is inserted into the beginning of the model's context.
+- Each prompt passed to `generate()` is wrapped in the appropriate prompt template. If you pass `allow_download=False`
+  to GPT4All or are using a model that is not from the official models list, you must pass a prompt template using the
+  `prompt_template` parameter of `chat_session()`.
 
-[models2.json]: https://github.com/nomic-ai/gpt4all/blob/main/gpt4all-chat/metadata/models2.json
+NOTE: If you do not use `chat_session()`, calls to `generate()` will not be wrapped in a prompt template. This will
+cause the model to *continue* the prompt instead of *answering* it. When in doubt, use a chat session, as many newer
+models are designed to be used exclusively with a prompt template.
+
+[models3.json]: https://github.com/nomic-ai/gpt4all/blob/main/gpt4all-chat/metadata/models3.json
 
 
 ### Streaming Generations
@@ -89,15 +85,16 @@ To interact with GPT4All responses as the model generates, use the `streaming=Tr
 === "GPT4All Streaming Example"
     ``` py
     from gpt4all import GPT4All
-    model = GPT4All("orca-mini-3b.ggmlv3.q4_0.bin")
+    model = GPT4All("orca-mini-3b-gguf2-q4_0.gguf")
     tokens = []
-    for token in model.generate("The capital of France is", max_tokens=20, streaming=True):
-        tokens.append(token)
+    with model.chat_session():
+        for token in model.generate("What is the capital of France?", streaming=True):
+            tokens.append(token)
     print(tokens)
     ```
 === "Output"
     ```
-    [' Paris', ' is', ' a', ' city', ' that', ' has', ' been', ' a', ' major', ' cultural', ' and', ' economic', ' center', ' for', ' over', ' ', '2', ',', '0', '0']
+    [' The', ' capital', ' of', ' France', ' is', ' Paris', '.']
     ```
 
 
@@ -131,20 +128,11 @@ generation; be sure to review all their descriptions.
 The model folder can be set with the `model_path` parameter when creating a `GPT4All` instance. The example below is
 is the same as if it weren't provided; that is, `~/.cache/gpt4all/` is the default folder.
 
-=== "GPT4All Model Folder Example"
-    ``` py
-    from pathlib import Path
-    from gpt4all import GPT4All
-    model = GPT4All(model_name='orca-mini-3b.ggmlv3.q4_0.bin',
-                    model_path=(Path.home() / '.cache' / 'gpt4all'),
-                    allow_download=False)
-    response = model.generate('my favorite 3 fruits are:', temp=0)
-    print(response)
-    ```
-=== "Output"
-    ```
-    My favorite three fruits are apples, bananas and oranges.
-    ```
+``` py
+from pathlib import Path
+from gpt4all import GPT4All
+model = GPT4All(model_name='orca-mini-3b-gguf2-q4_0.gguf', model_path=Path.home() / '.cache' / 'gpt4all')
+```
 
 If you want to point it at the chat GUI's default folder, it should be:
 === "macOS"
@@ -152,7 +140,7 @@ If you want to point it at the chat GUI's default folder, it should be:
     from pathlib import Path
     from gpt4all import GPT4All
 
-    model_name = 'orca-mini-3b.ggmlv3.q4_0.bin'
+    model_name = 'orca-mini-3b-gguf2-q4_0.gguf'
     model_path = Path.home() / 'Library' / 'Application Support' / 'nomic.ai' / 'GPT4All'
     model = GPT4All(model_name, model_path)
     ```
@@ -161,7 +149,7 @@ If you want to point it at the chat GUI's default folder, it should be:
     from pathlib import Path
     from gpt4all import GPT4All
     import os
-    model_name = 'orca-mini-3b.ggmlv3.q4_0.bin'
+    model_name = 'orca-mini-3b-gguf2-q4_0.gguf'
     model_path = Path(os.environ['LOCALAPPDATA']) / 'nomic.ai' / 'GPT4All'
     model = GPT4All(model_name, model_path)
     ```
@@ -170,7 +158,7 @@ If you want to point it at the chat GUI's default folder, it should be:
     from pathlib import Path
     from gpt4all import GPT4All
 
-    model_name = 'orca-mini-3b.ggmlv3.q4_0.bin'
+    model_name = 'orca-mini-3b-gguf2-q4_0.gguf'
     model_path = Path.home() / '.local' / 'share' / 'nomic.ai' / 'GPT4All'
     model = GPT4All(model_name, model_path)
     ```
@@ -179,22 +167,20 @@ Alternatively, you could also change the module's default model directory:
 
 ``` py
 from pathlib import Path
-import gpt4all.gpt4all
-gpt4all.gpt4all.DEFAULT_MODEL_DIRECTORY = Path.home() / 'my' / 'models-directory'
-from gpt4all import GPT4All
-model = GPT4All('orca-mini-3b.ggmlv3.q4_0.bin')
-...
+from gpt4all import GPT4All, gpt4all
+gpt4all.DEFAULT_MODEL_DIRECTORY = Path.home() / 'my' / 'models-directory'
+model = GPT4All('orca-mini-3b-gguf2-q4_0.gguf')
 ```
 
 
 ### Managing Templates
-Session templates can be customized when starting a `chat_session` context:
+When using a `chat_session()`, you may customize the system prompt, and set the prompt template if necessary:
 
 === "GPT4All Custom Session Templates Example"
     ``` py
     from gpt4all import GPT4All
-    model = GPT4All('ggml-Wizard-Vicuna-7B-Uncensored.ggmlv3.q4_1.bin')
-    system_template = 'A chat between a curious user and an artificial intelligence assistant.'
+    model = GPT4All('wizardlm-13b-v1.2.Q4_0.gguf')
+    system_template = 'A chat between a curious user and an artificial intelligence assistant.\n'
     # many models use triple hash '###' for keywords, Vicunas are simpler:
     prompt_template = 'USER: {0}\nASSISTANT: '
     with model.chat_session(system_template, prompt_template):
@@ -218,193 +204,38 @@ Session templates can be customized when starting a `chat_session` context:
     particles, making the sky appear blue to our eyes.
     ```
 
-To do the same outside a session, the input has to be formatted manually. For example:
-
-=== "GPT4All Templates Outside a Session Example"
-    ``` py
-    model = GPT4All('ggml-Wizard-Vicuna-7B-Uncensored.ggmlv3.q4_1.bin')
-    system_template = 'A chat between a curious user and an artificial intelligence assistant.'
-    prompt_template = 'USER: {0}\nASSISTANT: '
-    prompts = ['name 3 colors', 'now name 3 fruits', 'what were the 3 colors in your earlier response?']
-    first_input = system_template + prompt_template.format(prompts[0])
-    response = model.generate(first_input, temp=0)
-    print(response)
-    for prompt in prompts[1:]:
-        response = model.generate(prompt_template.format(prompt), temp=0)
-        print(response)
-    ```
-=== "Output"
-    ```
-    1) Red
-    2) Blue
-    3) Green
-
-    1. Apple
-    2. Banana
-    3. Orange
-
-    The colors in my previous response are blue, green and red.
-    ```
-
-Ultimately, the method `GPT4All._format_chat_prompt_template()` is responsible for formatting templates. It can be
-customized in a subclass. As an example:
-
-=== "Custom Subclass"
-    ``` py
-    from itertools import cycle
-    from gpt4all import GPT4All
-
-    class RotatingTemplateGPT4All(GPT4All):
-        def __init__(self, *args, **kwargs):
-            super().__init__(*args, **kwargs)
-            self._templates = [
-                "Respond like a pirate.",
-                "Respond like a politician.",
-                "Respond like a philosopher.",
-                "Respond like a Klingon.",
-            ]
-            self._cycling_templates = cycle(self._templates)
-
-        def _format_chat_prompt_template(
-            self,
-            messages: list,
-            default_prompt_header: str = "",
-            default_prompt_footer: str = "",
-        ) -> str:
-            full_prompt = default_prompt_header + "\n\n" if default_prompt_header != "" else ""
-            for message in messages:
-                if message["role"] == "user":
-                    user_message = f"USER: {message['content']} {next(self._cycling_templates)}\n"
-                    full_prompt += user_message
-                if message["role"] == "assistant":
-                    assistant_message = f"ASSISTANT: {message['content']}\n"
-                    full_prompt += assistant_message
-            full_prompt += "\n\n" + default_prompt_footer if default_prompt_footer != "" else ""
-            print(full_prompt)
-            return full_prompt
-    ```
-=== "GPT4All Custom Subclass Example"
-    ``` py
-    model = RotatingTemplateGPT4All('ggml-Wizard-Vicuna-7B-Uncensored.ggmlv3.q4_1.bin')
-    with model.chat_session():  # starting a session is optional in this example
-        response1 = model.generate("hi, who are you?")
-        print(response1)
-        print()
-        response2 = model.generate("what can you tell me about snakes?")
-        print(response2)
-        print()
-        response3 = model.generate("what's your opinion on Chess?")
-        print(response3)
-        print()
-        response4 = model.generate("tell me about ancient Rome.")
-        print(response4)
-    ```
-=== "Possible Output"
-    ```
-    USER: hi, who are you? Respond like a pirate.
-
-    Pirate: Ahoy there mateys! I be Cap'n Jack Sparrow of the Black Pearl.
-
-    USER: what can you tell me about snakes? Respond like a politician.
-
-    Politician: Snakes have been making headlines lately due to their ability to
-    slither into tight spaces and evade capture, much like myself during my last
-    election campaign. However, I believe that with proper education and
-    understanding of these creatures, we can work together towards creating a
-    safer environment for both humans and snakes alike.
-
-    USER: what's your opinion on Chess? Respond like a philosopher.
-
-    Philosopher: The game of chess is often used as an analogy to illustrate the
-    complexities of life and decision-making processes. However, I believe that it
-    can also be seen as a reflection of our own consciousness and subconscious mind.
-    Just as each piece on the board has its unique role to play in shaping the
-    outcome of the game, we too have different roles to fulfill in creating our own
-    personal narrative.
-
-    USER: tell me about ancient Rome. Respond like a Klingon.
-
-    Klingon: Ancient Rome was once a great empire that ruled over much of Europe and
-    the Mediterranean region. However, just as the Empire fell due to internal strife
-    and external threats, so too did my own house come crashing down when I failed to
-    protect our homeworld from invading forces.
-    ```
-
-
-### Introspection
-A less apparent feature is the capacity to log the final prompt that gets sent to the model. It relies on
-[Python's logging facilities][py-logging] implemented in the `pyllmodel` module at the `INFO` level. You can activate it
-for example with a `basicConfig`, which displays it on the standard error stream. It's worth mentioning that Python's
-logging infrastructure offers [many more customization options][py-logging-cookbook].
-
-[py-logging]: https://docs.python.org/3/howto/logging.html
-[py-logging-cookbook]: https://docs.python.org/3/howto/logging-cookbook.html
-
-=== "GPT4All Prompt Logging Example"
-    ``` py
-    import logging
-    from gpt4all import GPT4All
-    logging.basicConfig(level=logging.INFO)
-    model = GPT4All('nous-hermes-13b.ggmlv3.q4_0.bin')
-    with model.chat_session('You are a geography expert.\nBe terse.',
-                            '### Instruction:\n{0}\n### Response:\n'):
-        response = model.generate('who are you?', temp=0)
-        print(response)
-        response = model.generate('what are your favorite 3 mountains?', temp=0)
-        print(response)
-    ```
-=== "Output"
-    ```
-    INFO:gpt4all.pyllmodel:LLModel.prompt_model -- prompt:
-    You are a geography expert.
-    Be terse.
-
-    ### Instruction:
-    who are you?
-    ### Response:
-
-    ===/LLModel.prompt_model -- prompt/===
-    I am an AI-powered chatbot designed to assist users with their queries related to geographical information.
-    INFO:gpt4all.pyllmodel:LLModel.prompt_model -- prompt:
-    ### Instruction:
-    what are your favorite 3 mountains?
-    ### Response:
-
-    ===/LLModel.prompt_model -- prompt/===
-    1) Mount Everest - Located in the Himalayas, it is the highest mountain on Earth and a significant challenge for mountaineers.
-    2) Kangchenjunga - This mountain is located in the Himalayas and is the third-highest peak in the world after Mount Everest and K2.
-    3) Lhotse - Located in the Himalayas, it is the fourth highest mountain on Earth and offers a challenging climb for experienced mountaineers.
-    ```
-
 
 ### Without Online Connectivity
-To prevent GPT4All from accessing online resources, instantiate it with `allow_download=False`. This will disable both
-downloading missing models and [models2.json], which contains information about them. As a result, predefined templates
-are used instead of model-specific system and prompt templates:
+To prevent GPT4All from accessing online resources, instantiate it with `allow_download=False`. When using this flag,
+there will be no default system prompt by default, and you must specify the prompt template yourself.
 
-=== "GPT4All Default Templates Example"
+You can retrieve a model's default system prompt and prompt template with an online instance of GPT4All:
+
+=== "Prompt Template Retrieval"
     ``` py
     from gpt4all import GPT4All
-    model = GPT4All('ggml-mpt-7b-chat.bin', allow_download=False)
-    # when downloads are disabled, it will use the default templates:
-    print("default system template:", repr(model.config['systemPrompt']))
-    print("default prompt template:", repr(model.config['promptTemplate']))
-    print()
-    # even when inside a session:
-    with model.chat_session():
-        assert model.current_chat_session[0]['role'] == 'system'
-        print("session system template:", repr(model.current_chat_session[0]['content']))
-        print("session prompt template:", repr(model._current_prompt_template))
+    model = GPT4All('orca-mini-3b-gguf2-q4_0.gguf')
+    print(repr(model.config['systemPrompt']))
+    print(repr(model.config['promptTemplate']))
     ```
 === "Output"
-    ```
-    default system template: ''
-    default prompt template: '### Human: \n{0}\n### Assistant:\n'
-
-    session system template: ''
-    session prompt template: '### Human: \n{0}\n### Assistant:\n'
+    ```py
+    '### System:\nYou are an AI assistant that follows instruction extremely well. Help as much as you can.\n\n'
+    '### User:\n{0}\n### Response:\n'
     ```
 
+Then you can pass them explicitly when creating an offline instance:
+
+``` py
+from gpt4all import GPT4All
+model = GPT4All('orca-mini-3b-gguf2-q4_0.gguf', allow_download=False)
+
+system_prompt = '### System:\nYou are an AI assistant that follows instruction extremely well. Help as much as you can.\n\n'
+prompt_template = '### User:\n{0}\n\n### Response:\n'
+
+with model.chat_session(system_prompt=system_prompt, prompt_template=prompt_template):
+    ...
+```
 
 ### Interrupting Generation
 The simplest way to stop generation is to set a fixed upper limit with the `max_tokens` parameter.
@@ -414,7 +245,7 @@ If you know exactly when a model should stop responding, you can add a custom ca
 === "GPT4All Custom Stop Callback"
     ``` py
     from gpt4all import GPT4All
-    model = GPT4All('orca-mini-3b.ggmlv3.q4_0.bin')
+    model = GPT4All('orca-mini-3b-gguf2-q4_0.gguf')
 
     def stop_on_token_callback(token_id, token_string):
         # one sentence is enough:

gpt4all-bindings/python/docs/gpt4all_python_embedding.md

@@ -1,18 +1,41 @@
 # Embeddings
-GPT4All supports generating high quality embeddings of arbitrary length documents of text using a CPU optimized contrastively trained [Sentence Transformer](https://www.sbert.net/). These embeddings are comparable in quality for many tasks with OpenAI.
+GPT4All supports generating high quality embeddings of arbitrary length text using any embedding model supported by llama.cpp.
+
+An embedding is a vector representation of a piece of text. Embeddings are useful for tasks such as retrieval for
+question answering (including retrieval augmented generation or *RAG*), semantic similarity search, classification, and
+topic clustering.
+
+## Supported Embedding Models
+
+The following models have built-in support in Embed4All:
+
+| Name               | Embed4All `model_name`                               | Context Length | Embedding Length | File Size |
+|--------------------|------------------------------------------------------|---------------:|-----------------:|----------:|
+| [SBert]            | all‑MiniLM‑L6‑v2.gguf2.f16.gguf |            512 |              384 |    44 MiB |
+| [Nomic Embed v1]   | nomic‑embed‑text‑v1.f16.gguf    |           2048 |              768 |   262 MiB |
+| [Nomic Embed v1.5] | nomic‑embed‑text‑v1.5.f16.gguf  |           2048 |           64-768 |   262 MiB |
+
+The context length is the maximum number of word pieces, or *tokens*, that a model can embed at once. Embedding texts
+longer than a model's context length requires some kind of strategy; see [Embedding Longer Texts] for more information.
+
+The embedding length is the size of the vector returned by `Embed4All.embed`.
+
+[SBert]: https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2
+[Nomic Embed v1]: https://huggingface.co/nomic-ai/nomic-embed-text-v1
+[Nomic Embed v1.5]: https://huggingface.co/nomic-ai/nomic-embed-text-v1.5
+[Embedding Longer Texts]: #embedding-longer-texts
 
 ## Quickstart
-
 ```bash
 pip install gpt4all
 ```
 
-### Generating embeddings
-The embedding model will automatically be downloaded if not installed.
+### Generating Embeddings
+By default, embeddings will be generated on the CPU using all-MiniLM-L6-v2.
 
 === "Embed4All Example"
-    ``` py
-    from gpt4all import GPT4All, Embed4All
+    ```py
+    from gpt4all import Embed4All
     text = 'The quick brown fox jumps over the lazy dog'
     embedder = Embed4All()
     output = embedder.embed(text)
@@ -22,13 +45,131 @@ The embedding model will automatically be downloaded if not installed.
     ```
     [0.034696947783231735, -0.07192722707986832, 0.06923297047615051, ...]
     ```
-### Speed of embedding generation
-The following table lists the generation speed for text document captured on an Intel i913900HX CPU with DDR5 5600 running with 8 threads under stable load.
 
-| Tokens          | 128  | 512  | 2048 | 8129 | 16,384 |
-| --------------- | ---- | ---- | ---- | ---- | ---- |
-| Wall time (s)   | .02  | .08  | .24  | .96  | 1.9  |
-| Tokens / Second | 6508 | 6431 | 8622 | 8509 | 8369 |
+You can also use the GPU to accelerate the embedding model by specifying the `device` parameter. See the [GPT4All
+constructor] for more information.
+
+=== "GPU Example"
+    ```py
+    from gpt4all import Embed4All
+    text = 'The quick brown fox jumps over the lazy dog'
+    embedder = Embed4All(device='gpu')
+    output = embedder.embed(text)
+    print(output)
+    ```
+=== "Output"
+    ```
+    [0.034696947783231735, -0.07192722707986832, 0.06923297047615051, ...]
+    ```
+
+[GPT4All constructor]: gpt4all_python.md#gpt4all.gpt4all.GPT4All.__init__
+
+### Nomic Embed
+
+Embed4All has built-in support for Nomic's open-source embedding model, [Nomic Embed]. When using this model, you must
+specify the task type using the `prefix` argument. This may be one of `search_query`, `search_document`,
+`classification`, or `clustering`. For retrieval applications, you should prepend `search_document` for all of your
+documents and `search_query` for your queries. See the [Nomic Embedding Guide] for more info.
+
+=== "Nomic Embed Example"
+    ```py
+    from gpt4all import Embed4All
+    text = 'Who is Laurens van der Maaten?'
+    embedder = Embed4All('nomic-embed-text-v1.f16.gguf')
+    output = embedder.embed(text, prefix='search_query')
+    print(output)
+    ```
+=== "Output"
+    ```
+    [-0.013357644900679588, 0.027070969343185425, -0.0232995692640543, ...]
+    ```
+
+[Nomic Embed]: https://blog.nomic.ai/posts/nomic-embed-text-v1
+[Nomic Embedding Guide]: https://docs.nomic.ai/atlas/guides/embeddings#embedding-task-types
+
+### Embedding Longer Texts
+
+Embed4All accepts a parameter called `long_text_mode`. This controls the behavior of Embed4All for texts longer than the
+context length of the embedding model.
+
+In the default mode of "mean", Embed4All will break long inputs into chunks and average their embeddings to compute the
+final result.
+
+To change this behavior, you can set the `long_text_mode` parameter to "truncate", which will truncate the input to the
+sequence length of the model before generating a single embedding.
+
+=== "Truncation Example"
+    ```py
+    from gpt4all import Embed4All
+    text = 'The ' * 512 + 'The quick brown fox jumps over the lazy dog'
+    embedder = Embed4All()
+    output = embedder.embed(text, long_text_mode="mean")
+    print(output)
+    print()
+    output = embedder.embed(text, long_text_mode="truncate")
+    print(output)
+    ```
+=== "Output"
+    ```
+    [0.0039850445464253426, 0.04558328539133072, 0.0035536508075892925, ...]
+
+    [-0.009771130047738552, 0.034792833030223846, -0.013273917138576508, ...]
+    ```
+
+
+### Batching
+
+You can send multiple texts to Embed4All in a single call. This can give faster results when individual texts are
+significantly smaller than `n_ctx` tokens. (`n_ctx` defaults to 2048.)
+
+=== "Batching Example"
+    ```py
+    from gpt4all import Embed4All
+    texts = ['The quick brown fox jumps over the lazy dog', 'Foo bar baz']
+    embedder = Embed4All()
+    output = embedder.embed(texts)
+    print(output[0])
+    print()
+    print(output[1])
+    ```
+=== "Output"
+    ```
+    [0.03551332652568817, 0.06137588247656822, 0.05281158909201622, ...]
+
+    [-0.03879690542817116, 0.00013223080895841122, 0.023148687556385994, ...]
+    ```
+
+The number of texts that can be embedded in one pass of the model is proportional to the `n_ctx` parameter of Embed4All.
+Increasing it may increase batched embedding throughput if you have a fast GPU, at the cost of VRAM.
+```py
+embedder = Embed4All(n_ctx=4096, device='gpu')
+```
+
+
+### Resizable Dimensionality
+
+The embedding dimension of Nomic Embed v1.5 can be resized using the `dimensionality` parameter. This parameter supports
+any value between 64 and 768.
+
+Shorter embeddings use less storage, memory, and bandwidth with a small performance cost. See the [blog post] for more
+info.
+
+[blog post]: https://blog.nomic.ai/posts/nomic-embed-matryoshka
+
+=== "Matryoshka Example"
+    ```py
+    from gpt4all import Embed4All
+    text = 'The quick brown fox jumps over the lazy dog'
+    embedder = Embed4All('nomic-embed-text-v1.5.f16.gguf')
+    output = embedder.embed(text, dimensionality=64)
+    print(len(output))
+    print(output)
+    ```
+=== "Output"
+    ```
+    64
+    [-0.03567073494195938, 0.1301717758178711, -0.4333043396472931, ...]
+    ```
 
 
 ### API documentation

gpt4all-bindings/python/docs/index.md

@@ -9,7 +9,7 @@ GPT4All software is optimized to run inference of 3-13 billion parameter large l
 === "GPT4All Example"
     ``` py
     from gpt4all import GPT4All
-    model = GPT4All("orca-mini-3b.ggmlv3.q4_0.bin")
+    model = GPT4All("orca-mini-3b-gguf2-q4_0.gguf")
     output = model.generate("The capital of France is ", max_tokens=3)
     print(output)
     ```
@@ -38,7 +38,7 @@ The GPT4All software ecosystem is compatible with the following Transformer arch
 - `MPT` (including `Replit`)
 - `GPT-J`
 
-You can find an exhaustive list of supported models on the [website](https://gpt4all.io) or in the [models directory](https://raw.githubusercontent.com/nomic-ai/gpt4all/main/gpt4all-chat/metadata/models2.json)
+You can find an exhaustive list of supported models on the [website](https://gpt4all.io) or in the [models directory](https://raw.githubusercontent.com/nomic-ai/gpt4all/main/gpt4all-chat/metadata/models3.json)
 
 
 GPT4All models are artifacts produced through a process known as neural network quantization.

gpt4all-bindings/python/gpt4all/__init__.py

@@ -1,2 +1 @@
-from .gpt4all import Embed4All, GPT4All  # noqa
-from .pyllmodel import LLModel  # noqa
+from .gpt4all import Embed4All as Embed4All, GPT4All as GPT4All

gpt4all-bindings/python/gpt4all/_pyllmodel.py

@@ -1,34 +1,39 @@
-import atexit
+from __future__ import annotations
+
 import ctypes
-import importlib.resources
-import logging
 import os
 import platform
 import re
 import subprocess
 import sys
 import threading
-from contextlib import ExitStack
+from enum import Enum
 from queue import Queue
-from typing import Callable, Iterable, List
+from typing import Any, Callable, Generic, Iterable, TypeVar, overload
 
-logger: logging.Logger = logging.getLogger(__name__)
+if sys.version_info >= (3, 9):
+    import importlib.resources as importlib_resources
+else:
+    import importlib_resources
 
+if (3, 9) <= sys.version_info < (3, 11):
+    # python 3.9 broke generic TypedDict, python 3.11 fixed it
+    from typing_extensions import TypedDict
+else:
+    from typing import TypedDict
+
+EmbeddingsType = TypeVar('EmbeddingsType', bound='list[Any]')
 
-file_manager = ExitStack()
-atexit.register(file_manager.close)  # clean up files on exit
 
 # TODO: provide a config file to make this more robust
-MODEL_LIB_PATH = file_manager.enter_context(importlib.resources.as_file(
-    importlib.resources.files("gpt4all") / "llmodel_DO_NOT_MODIFY" / "build",
-))
+MODEL_LIB_PATH = importlib_resources.files("gpt4all") / "llmodel_DO_NOT_MODIFY" / "build"
 
 
 def load_llmodel_library():
     ext = {"Darwin": "dylib", "Linux": "so", "Windows": "dll"}[platform.system()]
 
     try:
-        # Linux, Windows, MinGW
+        # macOS, Linux, MinGW
         lib = ctypes.CDLL(str(MODEL_LIB_PATH / f"libllmodel.{ext}"))
     except FileNotFoundError:
         if ext != 'dll':
@@ -42,10 +47,6 @@ def load_llmodel_library():
 llmodel = load_llmodel_library()
 
 
-class LLModelError(ctypes.Structure):
-    _fields_ = [("message", ctypes.c_char_p), ("code", ctypes.c_int32)]
-
-
 class LLModelPromptContext(ctypes.Structure):
     _fields_ = [
         ("logits", ctypes.POINTER(ctypes.c_float)),
@@ -57,6 +58,7 @@ class LLModelPromptContext(ctypes.Structure):
         ("n_predict", ctypes.c_int32),
         ("top_k", ctypes.c_int32),
         ("top_p", ctypes.c_float),
+        ("min_p", ctypes.c_float),
         ("temp", ctypes.c_float),
         ("n_batch", ctypes.c_int32),
         ("repeat_penalty", ctypes.c_float),
@@ -77,15 +79,15 @@ class LLModelGPUDevice(ctypes.Structure):
 llmodel.llmodel_model_create.argtypes = [ctypes.c_char_p]
 llmodel.llmodel_model_create.restype = ctypes.c_void_p
 
-llmodel.llmodel_model_create2.argtypes = [ctypes.c_char_p, ctypes.c_char_p, ctypes.POINTER(LLModelError)]
+llmodel.llmodel_model_create2.argtypes = [ctypes.c_char_p, ctypes.c_char_p, ctypes.POINTER(ctypes.c_char_p)]
 llmodel.llmodel_model_create2.restype = ctypes.c_void_p
 
 llmodel.llmodel_model_destroy.argtypes = [ctypes.c_void_p]
 llmodel.llmodel_model_destroy.restype = None
 
-llmodel.llmodel_loadModel.argtypes = [ctypes.c_void_p, ctypes.c_char_p]
+llmodel.llmodel_loadModel.argtypes = [ctypes.c_void_p, ctypes.c_char_p, ctypes.c_int]
 llmodel.llmodel_loadModel.restype = ctypes.c_bool
-llmodel.llmodel_required_mem.argtypes = [ctypes.c_void_p, ctypes.c_char_p]
+llmodel.llmodel_required_mem.argtypes = [ctypes.c_void_p, ctypes.c_char_p, ctypes.c_int]
 llmodel.llmodel_required_mem.restype = ctypes.c_size_t
 llmodel.llmodel_isModelLoaded.argtypes = [ctypes.c_void_p]
 llmodel.llmodel_isModelLoaded.restype = ctypes.c_bool
@@ -97,21 +99,30 @@ RecalculateCallback = ctypes.CFUNCTYPE(ctypes.c_bool, ctypes.c_bool)
 llmodel.llmodel_prompt.argtypes = [
     ctypes.c_void_p,
     ctypes.c_char_p,
+    ctypes.c_char_p,
     PromptCallback,
     ResponseCallback,
     RecalculateCallback,
     ctypes.POINTER(LLModelPromptContext),
+    ctypes.c_bool,
+    ctypes.c_char_p,
 ]
 
 llmodel.llmodel_prompt.restype = None
 
-llmodel.llmodel_embedding.argtypes = [
+llmodel.llmodel_embed.argtypes = [
     ctypes.c_void_p,
-    ctypes.c_char_p,
+    ctypes.POINTER(ctypes.c_char_p),
     ctypes.POINTER(ctypes.c_size_t),
+    ctypes.c_char_p,
+    ctypes.c_int,
+    ctypes.POINTER(ctypes.c_size_t),
+    ctypes.c_bool,
+    ctypes.c_bool,
+    ctypes.POINTER(ctypes.c_char_p),
 ]
 
-llmodel.llmodel_embedding.restype = ctypes.POINTER(ctypes.c_float)
+llmodel.llmodel_embed.restype = ctypes.POINTER(ctypes.c_float)
 
 llmodel.llmodel_free_embedding.argtypes = [ctypes.POINTER(ctypes.c_float)]
 llmodel.llmodel_free_embedding.restype = None
@@ -125,7 +136,7 @@ llmodel.llmodel_set_implementation_search_path.restype = None
 llmodel.llmodel_threadCount.argtypes = [ctypes.c_void_p]
 llmodel.llmodel_threadCount.restype = ctypes.c_int32
 
-llmodel.llmodel_set_implementation_search_path(str(MODEL_LIB_PATH).replace("\\", r"\\").encode("utf-8"))
+llmodel.llmodel_set_implementation_search_path(str(MODEL_LIB_PATH).encode())
 
 llmodel.llmodel_available_gpu_devices.argtypes = [ctypes.c_void_p, ctypes.c_size_t, ctypes.POINTER(ctypes.c_int32)]
 llmodel.llmodel_available_gpu_devices.restype = ctypes.POINTER(LLModelGPUDevice)
@@ -150,124 +161,92 @@ def empty_response_callback(token_id: int, response: str) -> bool:
     return True
 
 
+# Symbol to terminate from generator
+class Sentinel(Enum):
+    TERMINATING_SYMBOL = 0
+
+
+class EmbedResult(Generic[EmbeddingsType], TypedDict):
+    embeddings: EmbeddingsType
+    n_prompt_tokens: int
+
+
 class LLModel:
     """
     Base class and universal wrapper for GPT4All language models
     built around llmodel C-API.
 
-    Attributes
+    Parameters
     ----------
-    model: llmodel_model
-        Ctype pointer to underlying model
-    model_name: str
-        Model name
+    model_path : str
+        Path to the model.
+    n_ctx : int
+        Maximum size of context window
+    ngl : int
+        Number of GPU layers to use (Vulkan)
     """
 
-    def __init__(self):
-        self.model = None
-        self.model_name = None
-        self.context = None
-        self.llmodel_lib = llmodel
-
+    def __init__(self, model_path: str, n_ctx: int, ngl: int):
+        self.model_path = model_path.encode()
+        self.n_ctx = n_ctx
+        self.ngl = ngl
+        self.context: LLModelPromptContext | None = None
         self.buffer = bytearray()
         self.buff_expecting_cont_bytes: int = 0
 
-    def __del__(self):
-        if self.model is not None:
-            self.llmodel_lib.llmodel_model_destroy(self.model)
+        # Construct a model implementation
+        err = ctypes.c_char_p()
+        model = llmodel.llmodel_model_create2(self.model_path, b"auto", ctypes.byref(err))
+        if model is None:
+            s = err.value
+            raise RuntimeError(f"Unable to instantiate model: {'null' if s is None else s.decode()}")
+        self.model = model
 
-    def memory_needed(self, model_path: str) -> int:
-        model_path_enc = model_path.encode("utf-8")
-        self.model = llmodel.llmodel_model_create(model_path_enc)
+    def __del__(self, llmodel=llmodel):
+        if hasattr(self, 'model'):
+            llmodel.llmodel_model_destroy(self.model)
 
-        if self.model is not None:
-            return llmodel.llmodel_required_mem(self.model, model_path_enc)
-        else:
-            raise ValueError("Unable to instantiate model")
-
-    def list_gpu(self, model_path: str) -> list:
-        """
-        Lists available GPU devices that satisfy the model's memory requirements.
-
-        Parameters
-        ----------
-        model_path : str
-            Path to the model.
-
-        Returns
-        -------
-        list
-            A list of LLModelGPUDevice structures representing available GPU devices.
-        """
-        if self.model is not None:
-            model_path_enc = model_path.encode("utf-8")
-            mem_required = llmodel.llmodel_required_mem(self.model, model_path_enc)
-        else:
-            mem_required = self.memory_needed(model_path)
+    def _list_gpu(self, mem_required: int) -> list[LLModelGPUDevice]:
         num_devices = ctypes.c_int32(0)
-        devices_ptr = self.llmodel_lib.llmodel_available_gpu_devices(self.model, mem_required, ctypes.byref(num_devices))
+        devices_ptr = llmodel.llmodel_available_gpu_devices(self.model, mem_required, ctypes.byref(num_devices))
         if not devices_ptr:
             raise ValueError("Unable to retrieve available GPU devices")
-        devices = [devices_ptr[i] for i in range(num_devices.value)]
-        return devices
+        return devices_ptr[:num_devices.value]
 
-    def init_gpu(self, model_path: str, device: str):
-        if self.model is not None:
-            model_path_enc = model_path.encode("utf-8")
-            mem_required = llmodel.llmodel_required_mem(self.model, model_path_enc)
-        else:
-            mem_required = self.memory_needed(model_path)
-        device_enc = device.encode("utf-8")
-        success = self.llmodel_lib.llmodel_gpu_init_gpu_device_by_string(self.model, mem_required, device_enc)
-        if not success:
-            # Retrieve all GPUs without considering memory requirements.
-            num_devices = ctypes.c_int32(0)
-            all_devices_ptr = self.llmodel_lib.llmodel_available_gpu_devices(self.model, 0, ctypes.byref(num_devices))
-            if not all_devices_ptr:
-                raise ValueError("Unable to retrieve list of all GPU devices")
-            all_gpus = [all_devices_ptr[i].name.decode('utf-8') for i in range(num_devices.value)]
+    def init_gpu(self, device: str):
+        mem_required = llmodel.llmodel_required_mem(self.model, self.model_path, self.n_ctx, self.ngl)
 
-            # Retrieve GPUs that meet the memory requirements using list_gpu
-            available_gpus = [device.name.decode('utf-8') for device in self.list_gpu(model_path)]
+        if llmodel.llmodel_gpu_init_gpu_device_by_string(self.model, mem_required, device.encode()):
+            return
 
-            # Identify GPUs that are unavailable due to insufficient memory or features
-            unavailable_gpus = set(all_gpus) - set(available_gpus)
+        # Retrieve all GPUs without considering memory requirements.
+        num_devices = ctypes.c_int32(0)
+        all_devices_ptr = llmodel.llmodel_available_gpu_devices(self.model, 0, ctypes.byref(num_devices))
+        if not all_devices_ptr:
+            raise ValueError("Unable to retrieve list of all GPU devices")
+        all_gpus = [d.name.decode() for d in all_devices_ptr[:num_devices.value]]
 
-            # Formulate the error message
-            error_msg = "Unable to initialize model on GPU: '{}'.".format(device)
-            error_msg += "\nAvailable GPUs: {}.".format(available_gpus)
-            error_msg += "\nUnavailable GPUs due to insufficient memory or features: {}.".format(unavailable_gpus)
-            raise ValueError(error_msg)
+        # Retrieve GPUs that meet the memory requirements using list_gpu
+        available_gpus = [device.name.decode() for device in self._list_gpu(mem_required)]
 
-    def load_model(self, model_path: str) -> bool:
+        # Identify GPUs that are unavailable due to insufficient memory or features
+        unavailable_gpus = set(all_gpus).difference(available_gpus)
+
+        # Formulate the error message
+        error_msg = "Unable to initialize model on GPU: '{}'.".format(device)
+        error_msg += "\nAvailable GPUs: {}.".format(available_gpus)
+        error_msg += "\nUnavailable GPUs due to insufficient memory or features: {}.".format(unavailable_gpus)
+        raise ValueError(error_msg)
+
+    def load_model(self) -> bool:
         """
         Load model from a file.
 
-        Parameters
-        ----------
-        model_path : str
-            Model filepath
-
         Returns
         -------
         True if model loaded successfully, False otherwise
         """
-        model_path_enc = model_path.encode("utf-8")
-        err = LLModelError()
-        self.model = llmodel.llmodel_model_create2(model_path_enc, b"auto", ctypes.byref(err))
-
-        if self.model is None:
-            raise ValueError(f"Unable to instantiate model: code={err.code}, {err.message.decode()}")
-
-        llmodel.llmodel_loadModel(self.model, model_path_enc)
-
-        filename = os.path.basename(model_path)
-        self.model_name = os.path.splitext(filename)[0]
-
-        if llmodel.llmodel_isModelLoaded(self.model):
-            return True
-        else:
-            return False
+        return llmodel.llmodel_loadModel(self.model, self.model_path, self.n_ctx, self.ngl)
 
     def set_thread_count(self, n_threads):
         if not llmodel.llmodel_isModelLoaded(self.model):
@@ -284,6 +263,7 @@ class LLModel:
         n_predict: int = 4096,
         top_k: int = 40,
         top_p: float = 0.9,
+        min_p: float = 0.0,
         temp: float = 0.1,
         n_batch: int = 8,
         repeat_penalty: float = 1.2,
@@ -292,7 +272,7 @@ class LLModel:
         reset_context: bool = False,
     ):
         if self.context is None:
-            self.context = LLModelPromptContext(
+            context = LLModelPromptContext(
                 logits_size=0,
                 tokens_size=0,
                 n_past=0,
@@ -300,48 +280,97 @@ class LLModel:
                 n_predict=n_predict,
                 top_k=top_k,
                 top_p=top_p,
+                min_p=min_p,
                 temp=temp,
                 n_batch=n_batch,
                 repeat_penalty=repeat_penalty,
                 repeat_last_n=repeat_last_n,
                 context_erase=context_erase,
             )
-        elif reset_context:
-            self.context.n_past = 0
+            self.context = context
+        else:
+            context = self.context
+            if reset_context:
+                self.context.n_past = 0
 
         self.context.n_predict = n_predict
         self.context.top_k = top_k
         self.context.top_p = top_p
+        self.context.min_p = min_p
         self.context.temp = temp
         self.context.n_batch = n_batch
         self.context.repeat_penalty = repeat_penalty
         self.context.repeat_last_n = repeat_last_n
         self.context.context_erase = context_erase
 
-    def generate_embedding(self, text: str) -> List[float]:
-        if not text:
-            raise ValueError("Text must not be None or empty")
+    @overload
+    def generate_embeddings(
+        self, text: str, prefix: str, dimensionality: int, do_mean: bool, atlas: bool,
+    ) -> EmbedResult[list[float]]: ...
+    @overload
+    def generate_embeddings(
+        self, text: list[str], prefix: str | None, dimensionality: int, do_mean: bool, atlas: bool,
+    ) -> EmbedResult[list[list[float]]]: ...
+    @overload
+    def generate_embeddings(
+        self, text: str | list[str], prefix: str | None, dimensionality: int, do_mean: bool, atlas: bool,
+    ) -> EmbedResult[list[Any]]: ...
 
+    def generate_embeddings(
+        self, text: str | list[str], prefix: str | None, dimensionality: int, do_mean: bool, atlas: bool,
+    ) -> EmbedResult[list[Any]]:
+        if not text:
+            raise ValueError("text must not be None or empty")
+
+        if (single_text := isinstance(text, str)):
+            text = [text]
+
+        # prepare input
         embedding_size = ctypes.c_size_t()
-        c_text = ctypes.c_char_p(text.encode('utf-8'))
-        embedding_ptr = llmodel.llmodel_embedding(self.model, c_text, ctypes.byref(embedding_size))
-        embedding_array = [embedding_ptr[i] for i in range(embedding_size.value)]
+        token_count = ctypes.c_size_t()
+        error = ctypes.c_char_p()
+        c_prefix = ctypes.c_char_p() if prefix is None else prefix.encode()
+        c_texts = (ctypes.c_char_p * (len(text) + 1))()
+        for i, t in enumerate(text):
+            c_texts[i] = t.encode()
+
+        # generate the embeddings
+        embedding_ptr = llmodel.llmodel_embed(
+            self.model, c_texts, ctypes.byref(embedding_size), c_prefix, dimensionality, ctypes.byref(token_count),
+            do_mean, atlas, ctypes.byref(error),
+        )
+
+        if not embedding_ptr:
+            msg = "(unknown error)" if error.value is None else error.value.decode()
+            raise RuntimeError(f'Failed to generate embeddings: {msg}')
+
+        # extract output
+        n_embd = embedding_size.value // len(text)
+        embedding_array = [
+            embedding_ptr[i:i + n_embd]
+            for i in range(0, embedding_size.value, n_embd)
+        ]
         llmodel.llmodel_free_embedding(embedding_ptr)
-        return list(embedding_array)
+
+        embeddings = embedding_array[0] if single_text else embedding_array
+        return {'embeddings': embeddings, 'n_prompt_tokens': token_count.value}
 
     def prompt_model(
         self,
         prompt: str,
+        prompt_template: str,
         callback: ResponseCallbackType,
         n_predict: int = 4096,
         top_k: int = 40,
         top_p: float = 0.9,
+        min_p: float = 0.0,
         temp: float = 0.1,
         n_batch: int = 8,
         repeat_penalty: float = 1.2,
         repeat_last_n: int = 10,
         context_erase: float = 0.75,
         reset_context: bool = False,
+        special: bool = False,
     ):
         """
         Generate response from model from a prompt.
@@ -361,20 +390,11 @@ class LLModel:
         self.buffer.clear()
         self.buff_expecting_cont_bytes = 0
 
-        logger.info(
-            "LLModel.prompt_model -- prompt:\n"
-            + "%s\n"
-            + "===/LLModel.prompt_model -- prompt/===",
-            prompt,
-        )
-
-        prompt_bytes = prompt.encode("utf-8")
-        prompt_ptr = ctypes.c_char_p(prompt_bytes)
-
         self._set_context(
             n_predict=n_predict,
             top_k=top_k,
             top_p=top_p,
+            min_p=min_p,
             temp=temp,
             n_batch=n_batch,
             repeat_penalty=repeat_penalty,
@@ -385,21 +405,21 @@ class LLModel:
 
         llmodel.llmodel_prompt(
             self.model,
-            prompt_ptr,
+            ctypes.c_char_p(prompt.encode()),
+            ctypes.c_char_p(prompt_template.encode()),
             PromptCallback(self._prompt_callback),
             ResponseCallback(self._callback_decoder(callback)),
             RecalculateCallback(self._recalculate_callback),
             self.context,
+            special,
+            ctypes.c_char_p(),
         )
 
 
     def prompt_model_streaming(
-        self, prompt: str, callback: ResponseCallbackType = empty_response_callback, **kwargs
+        self, prompt: str, prompt_template: str, callback: ResponseCallbackType = empty_response_callback, **kwargs
     ) -> Iterable[str]:
-        # Symbol to terminate from generator
-        TERMINATING_SYMBOL = object()
-
-        output_queue: Queue = Queue()
+        output_queue: Queue[str | Sentinel] = Queue()
 
         # Put response tokens into an output queue
         def _generator_callback_wrapper(callback: ResponseCallbackType) -> ResponseCallbackType:
@@ -414,15 +434,15 @@ class LLModel:
 
             return _generator_callback
 
-        def run_llmodel_prompt(prompt: str, callback: ResponseCallbackType, **kwargs):
-            self.prompt_model(prompt, callback, **kwargs)
-            output_queue.put(TERMINATING_SYMBOL)
+        def run_llmodel_prompt(prompt: str, prompt_template: str, callback: ResponseCallbackType, **kwargs):
+            self.prompt_model(prompt, prompt_template, callback, **kwargs)
+            output_queue.put(Sentinel.TERMINATING_SYMBOL)
 
         # Kick off llmodel_prompt in separate thread so we can return generator
         # immediately
         thread = threading.Thread(
             target=run_llmodel_prompt,
-            args=(prompt, _generator_callback_wrapper(callback)),
+            args=(prompt, prompt_template, _generator_callback_wrapper(callback)),
             kwargs=kwargs,
         )
         thread.start()
@@ -430,7 +450,7 @@ class LLModel:
         # Generator
         while True:
             response = output_queue.get()
-            if response is TERMINATING_SYMBOL:
+            if isinstance(response, Sentinel):
                 break
             yield response
 
@@ -453,7 +473,7 @@ class LLModel:
                 else: 
                     # beginning of a byte sequence
                     if len(self.buffer) > 0:
-                        decoded.append(self.buffer.decode('utf-8', 'replace'))
+                        decoded.append(self.buffer.decode(errors='replace'))
 
                         self.buffer.clear()
 
@@ -462,7 +482,7 @@ class LLModel:
 
                 if self.buff_expecting_cont_bytes <= 0: 
                     # received the whole sequence or an out of place continuation byte
-                    decoded.append(self.buffer.decode('utf-8', 'replace'))
+                    decoded.append(self.buffer.decode(errors='replace'))
 
                     self.buffer.clear()
                     self.buff_expecting_cont_bytes = 0

gpt4all-bindings/python/gpt4all/gpt4all.py

@@ -3,30 +3,37 @@ Python only API for running all GPT4All models.
 """
 from __future__ import annotations
 
+import hashlib
 import os
+import re
 import sys
 import time
+import warnings
 from contextlib import contextmanager
 from pathlib import Path
-from typing import Any, Dict, Iterable, List, Optional, Union
+from typing import TYPE_CHECKING, Any, Iterable, Literal, Protocol, overload
 
 import requests
 from requests.exceptions import ChunkedEncodingError
 from tqdm import tqdm
 from urllib3.exceptions import IncompleteRead, ProtocolError
 
-from . import pyllmodel
+from . import _pyllmodel
+from ._pyllmodel import EmbedResult as EmbedResult
+
+if TYPE_CHECKING:
+    from typing_extensions import TypeAlias
+
+if sys.platform == 'darwin':
+    import fcntl
 
 # TODO: move to config
-DEFAULT_MODEL_DIRECTORY = os.path.join(str(Path.home()), ".cache", "gpt4all").replace("\\", "\\\\")
+DEFAULT_MODEL_DIRECTORY = Path.home() / ".cache" / "gpt4all"
 
-DEFAULT_MODEL_CONFIG = {
-    "systemPrompt": "",
-    "promptTemplate": "### Human: \n{0}\n### Assistant:\n",
-}
+DEFAULT_PROMPT_TEMPLATE = "### Human:\n{0}\n\n### Assistant:\n"
 
-ConfigType = Dict[str, str]
-MessageType = Dict[str, str]
+ConfigType: TypeAlias = 'dict[str, Any]'
+MessageType: TypeAlias = 'dict[str, str]'
 
 
 class Embed4All:
@@ -34,26 +41,99 @@ class Embed4All:
     Python class that handles embeddings for GPT4All.
     """
 
-    def __init__(self, model_name: Optional[str] = None, n_threads: Optional[int] = None, **kwargs):
+    MIN_DIMENSIONALITY = 64
+
+    def __init__(self, model_name: str | None = None, n_threads: int | None = None, **kwargs):
         """
         Constructor
 
         Args:
             n_threads: number of CPU threads used by GPT4All. Default is None, then the number of threads are determined automatically.
         """
-        self.gpt4all = GPT4All(model_name or 'all-MiniLM-L6-v2-f16.gguf', n_threads=n_threads, **kwargs)
+        if model_name is None:
+            model_name = 'all-MiniLM-L6-v2.gguf2.f16.gguf'
+        self.gpt4all = GPT4All(model_name, n_threads=n_threads, **kwargs)
 
-    def embed(self, text: str) -> List[float]:
+    # return_dict=False
+    @overload
+    def embed(
+        self, text: str, *, prefix: str | None = ..., dimensionality: int | None = ..., long_text_mode: str = ...,
+        return_dict: Literal[False] = ..., atlas: bool = ...,
+    ) -> list[float]: ...
+    @overload
+    def embed(
+        self, text: list[str], *, prefix: str | None = ..., dimensionality: int | None = ..., long_text_mode: str = ...,
+        return_dict: Literal[False] = ..., atlas: bool = ...,
+    ) -> list[list[float]]: ...
+    @overload
+    def embed(
+        self, text: str | list[str], *, prefix: str | None = ..., dimensionality: int | None = ...,
+        long_text_mode: str = ..., return_dict: Literal[False] = ..., atlas: bool = ...,
+    ) -> list[Any]: ...
+
+    # return_dict=True
+    @overload
+    def embed(
+        self, text: str, *, prefix: str | None = ..., dimensionality: int | None = ..., long_text_mode: str = ...,
+        return_dict: Literal[True], atlas: bool = ...,
+    ) -> EmbedResult[list[float]]: ...
+    @overload
+    def embed(
+        self, text: list[str], *, prefix: str | None = ..., dimensionality: int | None = ..., long_text_mode: str = ...,
+        return_dict: Literal[True], atlas: bool = ...,
+    ) -> EmbedResult[list[list[float]]]: ...
+    @overload
+    def embed(
+        self, text: str | list[str], *, prefix: str | None = ..., dimensionality: int | None = ...,
+        long_text_mode: str = ..., return_dict: Literal[True], atlas: bool = ...,
+    ) -> EmbedResult[list[Any]]: ...
+
+    # return type unknown
+    @overload
+    def embed(
+        self, text: str | list[str], *, prefix: str | None = ..., dimensionality: int | None = ...,
+        long_text_mode: str = ..., return_dict: bool = ..., atlas: bool = ...,
+    ) -> Any: ...
+
+    def embed(
+        self, text: str | list[str], *, prefix: str | None = None, dimensionality: int | None = None,
+        long_text_mode: str = "mean", return_dict: bool = False, atlas: bool = False,
+    ) -> Any:
         """
-        Generate an embedding.
+        Generate one or more embeddings.
 
         Args:
-            text: The text document to generate an embedding for.
+            text: A text or list of texts to generate embeddings for.
+            prefix: The model-specific prefix representing the embedding task, without the trailing colon. For Nomic
+                Embed, this can be `search_query`, `search_document`, `classification`, or `clustering`. Defaults to
+                `search_document` or equivalent if known; otherwise, you must explicitly pass a prefix or an empty
+                string if none applies.
+            dimensionality: The embedding dimension, for use with Matryoshka-capable models. Defaults to full-size.
+            long_text_mode: How to handle texts longer than the model can accept. One of `mean` or `truncate`.
+            return_dict: Return the result as a dict that includes the number of prompt tokens processed.
+            atlas: Try to be fully compatible with the Atlas API. Currently, this means texts longer than 8192 tokens
+                with long_text_mode="mean" will raise an error. Disabled by default.
 
         Returns:
-            An embedding of your document of text.
+            With return_dict=False, an embedding or list of embeddings of your text(s).
+            With return_dict=True, a dict with keys 'embeddings' and 'n_prompt_tokens'.
         """
-        return self.gpt4all.model.generate_embedding(text)
+        if dimensionality is None:
+            dimensionality = -1
+        else:
+            if dimensionality <= 0:
+                raise ValueError(f'Dimensionality must be None or a positive integer, got {dimensionality}')
+            if dimensionality < self.MIN_DIMENSIONALITY:
+                warnings.warn(
+                    f'Dimensionality {dimensionality} is less than the suggested minimum of {self.MIN_DIMENSIONALITY}.'
+                    ' Performance may be degraded.'
+                )
+        try:
+            do_mean = {"mean": True, "truncate": False}[long_text_mode]
+        except KeyError:
+            raise ValueError(f"Long text mode must be one of 'mean' or 'truncate', got {long_text_mode!r}")
+        result = self.gpt4all.model.generate_embeddings(text, prefix, dimensionality, do_mean, atlas)
+        return result if return_dict else result['embeddings']
 
 
 class GPT4All:
@@ -64,18 +144,20 @@ class GPT4All:
     def __init__(
         self,
         model_name: str,
-        model_path: Optional[Union[str, os.PathLike[str]]] = None,
-        model_type: Optional[str] = None,
+        model_path: str | os.PathLike[str] | None = None,
+        model_type: str | None = None,
         allow_download: bool = True,
-        n_threads: Optional[int] = None,
-        device: Optional[str] = "cpu",
+        n_threads: int | None = None,
+        device: str | None = "cpu",
+        n_ctx: int = 2048,
+        ngl: int = 100,
         verbose: bool = False,
     ):
         """
         Constructor
 
         Args:
-            model_name: Name of GPT4All or custom model. Including ".bin" file extension is optional but encouraged.
+            model_name: Name of GPT4All or custom model. Including ".gguf" file extension is optional but encouraged.
             model_path: Path to directory containing model file or, if file does not exist, where to download model.
                 Default is None, in which case models will be stored in `~/.cache/gpt4all/`.
             model_type: Model architecture. This argument currently does not have any functionality and is just used as
@@ -90,40 +172,46 @@ class GPT4All:
                 Default is "cpu".
 
                 Note: If a selected GPU device does not have sufficient RAM to accommodate the model, an error will be thrown, and the GPT4All instance will be rendered invalid. It's advised to ensure the device has enough memory before initiating the model.
+            n_ctx: Maximum size of context window
+            ngl: Number of GPU layers to use (Vulkan)
+            verbose: If True, print debug messages.
         """
         self.model_type = model_type
-        self.model = pyllmodel.LLModel()
         # Retrieve model and download if allowed
         self.config: ConfigType = self.retrieve_model(model_name, model_path=model_path, allow_download=allow_download, verbose=verbose)
-        if device is not None:
-            if device != "cpu":
-                self.model.init_gpu(model_path=self.config["path"], device=device)
-        self.model.load_model(self.config["path"])
+        self.model = _pyllmodel.LLModel(self.config["path"], n_ctx, ngl)
+        if device is not None and device != "cpu":
+            self.model.init_gpu(device)
+        self.model.load_model()
         # Set n_threads
         if n_threads is not None:
             self.model.set_thread_count(n_threads)
 
-        self._is_chat_session_activated: bool = False
-        self.current_chat_session: List[MessageType] = empty_chat_session()
+        self._history: list[MessageType] | None = None
         self._current_prompt_template: str = "{0}"
 
+    @property
+    def current_chat_session(self) -> list[MessageType] | None:
+        return None if self._history is None else list(self._history)
+
     @staticmethod
-    def list_models() -> List[ConfigType]:
+    def list_models() -> list[ConfigType]:
         """
-        Fetch model list from https://gpt4all.io/models/models2.json.
+        Fetch model list from https://gpt4all.io/models/models3.json.
 
         Returns:
             Model list in JSON format.
         """
-        resp = requests.get("https://gpt4all.io/models/models2.json")
+        resp = requests.get("https://gpt4all.io/models/models3.json")
         if resp.status_code != 200:
             raise ValueError(f'Request failed: HTTP {resp.status_code} {resp.reason}')
         return resp.json()
 
-    @staticmethod
+    @classmethod
     def retrieve_model(
+        cls,
         model_name: str,
-        model_path: Optional[Union[str, os.PathLike[str]]] = None,
+        model_path: str | os.PathLike[str] | None = None,
         allow_download: bool = True,
         verbose: bool = False,
     ) -> ConfigType:
@@ -141,94 +229,92 @@ class GPT4All:
             Model config.
         """
 
-        model_filename = append_bin_suffix_if_missing(model_name)
+        model_filename = append_extension_if_missing(model_name)
 
         # get the config for the model
-        config: ConfigType = DEFAULT_MODEL_CONFIG
+        config: ConfigType = {}
         if allow_download:
-            available_models = GPT4All.list_models()
+            available_models = cls.list_models()
 
             for m in available_models:
                 if model_filename == m["filename"]:
+                    tmpl = m.get("promptTemplate", DEFAULT_PROMPT_TEMPLATE)
+                    # change to Python-style formatting
+                    m["promptTemplate"] = tmpl.replace("%1", "{0}", 1).replace("%2", "{1}", 1)
                     config.update(m)
-                    config["systemPrompt"] = config["systemPrompt"].strip()
-                    config["promptTemplate"] = config["promptTemplate"].replace(
-                        "%1", "{0}", 1
-                    )  # change to Python-style formatting
                     break
 
         # Validate download directory
         if model_path is None:
             try:
                 os.makedirs(DEFAULT_MODEL_DIRECTORY, exist_ok=True)
-            except OSError as exc:
-                raise ValueError(
-                    f"Failed to create model download directory at {DEFAULT_MODEL_DIRECTORY}: {exc}. "
-                    "Please specify model_path."
-                )
+            except OSError as e:
+                raise RuntimeError("Failed to create model download directory") from e
             model_path = DEFAULT_MODEL_DIRECTORY
         else:
-            model_path = str(model_path).replace("\\", "\\\\")
+            model_path = Path(model_path)
 
-        if not os.path.exists(model_path):
-            raise ValueError(f"Invalid model directory: {model_path}")
+        if not model_path.exists():
+            raise FileNotFoundError(f"Model directory does not exist: {model_path!r}")
 
-        model_dest = os.path.join(model_path, model_filename).replace("\\", "\\\\")
-        if os.path.exists(model_dest):
-            config.pop("url", None)
-            config["path"] = model_dest
+        model_dest = model_path / model_filename
+        if model_dest.exists():
+            config["path"] = str(model_dest)
             if verbose:
-                print("Found model file at", model_dest, file=sys.stderr)
-
-        # If model file does not exist, download
+                print(f"Found model file at {str(model_dest)!r}", file=sys.stderr)
         elif allow_download:
-            url = config.pop("url", None)
-
-            config["path"] = GPT4All.download_model(model_filename, model_path, verbose=verbose, url=url)
+            # If model file does not exist, download
+            filesize = config.get("filesize")
+            config["path"] = str(cls.download_model(
+                model_filename, model_path, verbose=verbose, url=config.get("url"),
+                expected_size=None if filesize is None else int(filesize), expected_md5=config.get("md5sum"),
+            ))
         else:
-            raise ValueError("Failed to retrieve model")
+            raise FileNotFoundError(f"Model file does not exist: {model_dest!r}")
 
         return config
 
     @staticmethod
     def download_model(
         model_filename: str,
-        model_path: Union[str, os.PathLike[str]],
+        model_path: str | os.PathLike[str],
         verbose: bool = True,
-        url: Optional[str] = None,
-    ) -> str:
+        url: str | None = None,
+        expected_size: int | None = None,
+        expected_md5: str | None = None,
+    ) -> str | os.PathLike[str]:
         """
         Download model from https://gpt4all.io.
 
         Args:
-            model_filename: Filename of model (with .bin extension).
+            model_filename: Filename of model (with .gguf extension).
             model_path: Path to download model to.
             verbose: If True (default), print debug messages.
             url: the models remote url (e.g. may be hosted on HF)
+            expected_size: The expected size of the download.
+            expected_md5: The expected MD5 hash of the download.
 
         Returns:
             Model file destination.
         """
 
-        def get_download_url(model_filename):
-            if url:
-                return url
-            return f"https://gpt4all.io/models/{model_filename}"
-
         # Download model
-        download_path = os.path.join(model_path, model_filename).replace("\\", "\\\\")
-        download_url = get_download_url(model_filename)
+        if url is None:
+            url = f"https://gpt4all.io/models/gguf/{model_filename}"
 
         def make_request(offset=None):
             headers = {}
             if offset:
                 print(f"\nDownload interrupted, resuming from byte position {offset}", file=sys.stderr)
                 headers['Range'] = f'bytes={offset}-'  # resume incomplete response
-            response = requests.get(download_url, stream=True, headers=headers)
+                headers["Accept-Encoding"] = "identity"  # Content-Encoding changes meaning of ranges
+            response = requests.get(url, stream=True, headers=headers)
             if response.status_code not in (200, 206):
                 raise ValueError(f'Request failed: HTTP {response.status_code} {response.reason}')
             if offset and (response.status_code != 206 or str(offset) not in response.headers.get('Content-Range', '')):
                 raise ValueError('Connection was interrupted and server does not support range requests')
+            if (enc := response.headers.get("Content-Encoding")) is not None:
+                raise ValueError(f"Expected identity Content-Encoding, got {enc}")
             return response
 
         response = make_request()
@@ -236,60 +322,109 @@ class GPT4All:
         total_size_in_bytes = int(response.headers.get("content-length", 0))
         block_size = 2**20  # 1 MB
 
-        with open(download_path, "wb") as file, \
-                tqdm(total=total_size_in_bytes, unit="iB", unit_scale=True) as progress_bar:
+        partial_path = Path(model_path) / (model_filename + ".part")
+
+        with open(partial_path, "w+b") as partf:
             try:
-                while True:
-                    last_progress = progress_bar.n
-                    try:
-                        for data in response.iter_content(block_size):
-                            file.write(data)
-                            progress_bar.update(len(data))
-                    except ChunkedEncodingError as cee:
-                        if cee.args and isinstance(pe := cee.args[0], ProtocolError):
-                            if len(pe.args) >= 2 and isinstance(ir := pe.args[1], IncompleteRead):
-                                assert progress_bar.n <= ir.partial  # urllib3 may be ahead of us but never behind
-                                # the socket was closed during a read - retry
-                                response = make_request(progress_bar.n)
-                                continue
-                        raise
-                    if total_size_in_bytes != 0 and progress_bar.n < total_size_in_bytes:
-                        if progress_bar.n == last_progress:
-                            raise RuntimeError('Download not making progress, aborting.')
-                        # server closed connection prematurely - retry
-                        response = make_request(progress_bar.n)
-                        continue
-                    break
-            except Exception:
+                with tqdm(desc="Downloading", total=total_size_in_bytes, unit="iB", unit_scale=True) as progress_bar:
+                    while True:
+                        last_progress = progress_bar.n
+                        try:
+                            for data in response.iter_content(block_size):
+                                partf.write(data)
+                                progress_bar.update(len(data))
+                        except ChunkedEncodingError as cee:
+                            if cee.args and isinstance(pe := cee.args[0], ProtocolError):
+                                if len(pe.args) >= 2 and isinstance(ir := pe.args[1], IncompleteRead):
+                                    assert progress_bar.n <= ir.partial  # urllib3 may be ahead of us but never behind
+                                    # the socket was closed during a read - retry
+                                    response = make_request(progress_bar.n)
+                                    continue
+                            raise
+                        if total_size_in_bytes != 0 and progress_bar.n < total_size_in_bytes:
+                            if progress_bar.n == last_progress:
+                                raise RuntimeError("Download not making progress, aborting.")
+                            # server closed connection prematurely - retry
+                            response = make_request(progress_bar.n)
+                            continue
+                        break
+
+                # verify file integrity
+                file_size = partf.tell()
+                if expected_size is not None and file_size != expected_size:
+                    raise ValueError(f"Expected file size of {expected_size} bytes, got {file_size}")
+                if expected_md5 is not None:
+                    partf.seek(0)
+                    hsh = hashlib.md5()
+                    with tqdm(desc="Verifying", total=file_size, unit="iB", unit_scale=True) as bar:
+                        while chunk := partf.read(block_size):
+                            hsh.update(chunk)
+                            bar.update(len(chunk))
+                    if hsh.hexdigest() != expected_md5.lower():
+                        raise ValueError(f"Expected MD5 hash of {expected_md5!r}, got {hsh.hexdigest()!r}")
+            except:
                 if verbose:
                     print("Cleaning up the interrupted download...", file=sys.stderr)
                 try:
-                    os.remove(download_path)
+                    os.remove(partial_path)
                 except OSError:
                     pass
                 raise
 
-        if os.name == 'nt':
-            time.sleep(2)  # Sleep for a little bit so Windows can remove file lock
+            # flush buffers and sync the inode
+            partf.flush()
+            _fsync(partf)
+
+        # move to final destination
+        download_path = Path(model_path) / model_filename
+        try:
+            os.rename(partial_path, download_path)
+        except FileExistsError:
+            try:
+                os.remove(partial_path)
+            except OSError:
+                pass
+            raise
 
         if verbose:
-            print("Model downloaded at:", download_path, file=sys.stderr)
+            print(f"Model downloaded to {str(download_path)!r}", file=sys.stderr)
         return download_path
 
+    @overload
+    def generate(
+        self, prompt: str, *, max_tokens: int = ..., temp: float = ..., top_k: int = ..., top_p: float = ...,
+        min_p: float = ..., repeat_penalty: float = ..., repeat_last_n: int = ..., n_batch: int = ...,
+        n_predict: int | None = ..., streaming: Literal[False] = ..., callback: _pyllmodel.ResponseCallbackType = ...,
+    ) -> str: ...
+    @overload
+    def generate(
+        self, prompt: str, *, max_tokens: int = ..., temp: float = ..., top_k: int = ..., top_p: float = ...,
+        min_p: float = ..., repeat_penalty: float = ..., repeat_last_n: int = ..., n_batch: int = ...,
+        n_predict: int | None = ..., streaming: Literal[True], callback: _pyllmodel.ResponseCallbackType = ...,
+    ) -> Iterable[str]: ...
+    @overload
+    def generate(
+        self, prompt: str, *, max_tokens: int = ..., temp: float = ..., top_k: int = ..., top_p: float = ...,
+        min_p: float = ..., repeat_penalty: float = ..., repeat_last_n: int = ..., n_batch: int = ...,
+        n_predict: int | None = ..., streaming: bool, callback: _pyllmodel.ResponseCallbackType = ...,
+    ) -> Any: ...
+
     def generate(
         self,
         prompt: str,
+        *,
         max_tokens: int = 200,
         temp: float = 0.7,
         top_k: int = 40,
         top_p: float = 0.4,
+        min_p: float = 0.0,
         repeat_penalty: float = 1.18,
         repeat_last_n: int = 64,
         n_batch: int = 8,
-        n_predict: Optional[int] = None,
+        n_predict: int | None = None,
         streaming: bool = False,
-        callback: pyllmodel.ResponseCallbackType = pyllmodel.empty_response_callback,
-    ) -> Union[str, Iterable[str]]:
+        callback: _pyllmodel.ResponseCallbackType = _pyllmodel.empty_response_callback,
+    ) -> Any:
         """
         Generate outputs from any GPT4All model.
 
@@ -299,6 +434,7 @@ class GPT4All:
             temp: The model temperature. Larger values increase creativity but decrease factuality.
             top_k: Randomly sample from the top_k most likely tokens at each generation step. Set this to 1 for greedy decoding.
             top_p: Randomly sample at each generation step from the top most likely tokens whose probabilities add up to top_p.
+            min_p: Randomly sample at each generation step from the top most likely tokens whose probabilities are at least min_p.
             repeat_penalty: Penalize the model for repetition. Higher values result in less repetition.
             repeat_last_n: How far in the models generation history to apply the repeat penalty.
             n_batch: Number of prompt tokens processed in parallel. Larger values decrease latency but increase resource requirements.
@@ -311,44 +447,60 @@ class GPT4All:
         """
 
         # Preparing the model request
-        generate_kwargs: Dict[str, Any] = dict(
+        generate_kwargs: dict[str, Any] = dict(
             temp=temp,
             top_k=top_k,
             top_p=top_p,
+            min_p=min_p,
             repeat_penalty=repeat_penalty,
             repeat_last_n=repeat_last_n,
             n_batch=n_batch,
             n_predict=n_predict if n_predict is not None else max_tokens,
         )
 
-        if self._is_chat_session_activated:
+        if self._history is not None:
             # check if there is only one message, i.e. system prompt:
-            generate_kwargs["reset_context"] = len(self.current_chat_session) == 1
-            self.current_chat_session.append({"role": "user", "content": prompt})
+            reset = len(self._history) == 1
+            generate_kwargs["reset_context"] = reset
+            self._history.append({"role": "user", "content": prompt})
 
-            prompt = self._format_chat_prompt_template(
-                messages=self.current_chat_session[-1:],
-                default_prompt_header=self.current_chat_session[0]["content"]
-                if generate_kwargs["reset_context"]
-                else "",
-            )
+            fct_func = self._format_chat_prompt_template.__func__  # type: ignore[attr-defined]
+            if fct_func is GPT4All._format_chat_prompt_template:
+                if reset:
+                    # ingest system prompt
+                    self.model.prompt_model(self._history[0]["content"], "%1",
+                                            _pyllmodel.empty_response_callback,
+                                            n_batch=n_batch, n_predict=0, special=True)
+                prompt_template = self._current_prompt_template.format("%1", "%2")
+            else:
+                warnings.warn(
+                    "_format_chat_prompt_template is deprecated. Please use a chat session with a prompt template.",
+                    DeprecationWarning,
+                )
+                # special tokens won't be processed
+                prompt = self._format_chat_prompt_template(
+                    self._history[-1:],
+                    self._history[0]["content"] if reset else "",
+                )
+                prompt_template = "%1"
         else:
+            prompt_template = "%1"
             generate_kwargs["reset_context"] = True
 
         # Prepare the callback, process the model response
-        output_collector: List[MessageType]
+        output_collector: list[MessageType]
         output_collector = [
             {"content": ""}
-        ]  # placeholder for the self.current_chat_session if chat session is not activated
+        ]  # placeholder for the self._history if chat session is not activated
 
-        if self._is_chat_session_activated:
-            self.current_chat_session.append({"role": "assistant", "content": ""})
-            output_collector = self.current_chat_session
+        if self._history is not None:
+            self._history.append({"role": "assistant", "content": ""})
+            output_collector = self._history
 
         def _callback_wrapper(
-            callback: pyllmodel.ResponseCallbackType,
-            output_collector: List[MessageType],
-        ) -> pyllmodel.ResponseCallbackType:
+            callback: _pyllmodel.ResponseCallbackType,
+            output_collector: list[MessageType],
+        ) -> _pyllmodel.ResponseCallbackType:
             def _callback(token_id: int, response: str) -> bool:
                 nonlocal callback, output_collector
 
@@ -361,14 +513,16 @@ class GPT4All:
         # Send the request to the model
         if streaming:
             return self.model.prompt_model_streaming(
-                prompt=prompt,
-                callback=_callback_wrapper(callback, output_collector),
+                prompt,
+                prompt_template,
+                _callback_wrapper(callback, output_collector),
                 **generate_kwargs,
             )
 
         self.model.prompt_model(
-            prompt=prompt,
-            callback=_callback_wrapper(callback, output_collector),
+            prompt,
+            prompt_template,
+            _callback_wrapper(callback, output_collector),
             **generate_kwargs,
         )
 
@@ -377,8 +531,8 @@ class GPT4All:
     @contextmanager
     def chat_session(
         self,
-        system_prompt: str = "",
-        prompt_template: str = "",
+        system_prompt: str | None = None,
+        prompt_template: str | None = None,
     ):
         """
         Context manager to hold an inference optimized chat session with a GPT4All model.
@@ -387,21 +541,32 @@ class GPT4All:
             system_prompt: An initial instruction for the model.
             prompt_template: Template for the prompts with {0} being replaced by the user message.
         """
-        # Code to acquire resource, e.g.:
-        self._is_chat_session_activated = True
-        self.current_chat_session = empty_chat_session(system_prompt or self.config["systemPrompt"])
-        self._current_prompt_template = prompt_template or self.config["promptTemplate"]
+
+        if system_prompt is None:
+            system_prompt = self.config.get("systemPrompt", "")
+
+        if prompt_template is None:
+            if (tmpl := self.config.get("promptTemplate")) is None:
+                warnings.warn("Use of a sideloaded model or allow_download=False without specifying a prompt template "
+                              "is deprecated. Defaulting to Alpaca.", DeprecationWarning)
+                tmpl = DEFAULT_PROMPT_TEMPLATE
+            prompt_template = tmpl
+
+        if re.search(r"%1(?![0-9])", prompt_template):
+            raise ValueError("Prompt template containing a literal '%1' is not supported. For a prompt "
+                             "placeholder, please use '{0}' instead.")
+
+        self._history = [{"role": "system", "content": system_prompt}]
+        self._current_prompt_template = prompt_template
         try:
             yield self
         finally:
-            # Code to release resource, e.g.:
-            self._is_chat_session_activated = False
-            self.current_chat_session = empty_chat_session()
+            self._history = None
             self._current_prompt_template = "{0}"
 
     def _format_chat_prompt_template(
         self,
-        messages: List[MessageType],
+        messages: list[MessageType],
         default_prompt_header: str = "",
         default_prompt_footer: str = "",
     ) -> str:
@@ -419,24 +584,6 @@ class GPT4All:
             Formatted prompt.
         """
 
-        if isinstance(default_prompt_header, bool):
-            import warnings
-
-            warnings.warn(
-                "Using True/False for the 'default_prompt_header' is deprecated. Use a string instead.",
-                DeprecationWarning,
-            )
-            default_prompt_header = ""
-
-        if isinstance(default_prompt_footer, bool):
-            import warnings
-
-            warnings.warn(
-                "Using True/False for the 'default_prompt_footer' is deprecated. Use a string instead.",
-                DeprecationWarning,
-            )
-            default_prompt_footer = ""
-
         full_prompt = default_prompt_header + "\n\n" if default_prompt_header != "" else ""
 
         for message in messages:
@@ -452,11 +599,23 @@ class GPT4All:
         return full_prompt
 
 
-def empty_chat_session(system_prompt: str = "") -> List[MessageType]:
-    return [{"role": "system", "content": system_prompt}]
-
-
-def append_bin_suffix_if_missing(model_name):
+def append_extension_if_missing(model_name):
     if not model_name.endswith((".bin", ".gguf")):
-        model_name += ".bin"
+        model_name += ".gguf"
     return model_name
+
+
+class _HasFileno(Protocol):
+    def fileno(self) -> int: ...
+
+
+def _fsync(fd: int | _HasFileno) -> None:
+    if sys.platform == 'darwin':
+        # Apple's fsync does not flush the drive write cache
+        try:
+            fcntl.fcntl(fd, fcntl.F_FULLFSYNC)
+        except OSError:
+            pass  # fall back to fsync
+        else:
+            return
+    os.fsync(fd)

gpt4all-bindings/python/gpt4all/tests/test_embed_timings.py

@@ -1,3 +1,4 @@
+#!/usr/bin/env python3
 import sys
 import time
 from io import StringIO

gpt4all-bindings/python/gpt4all/tests/test_gpt4all.py

@@ -8,7 +8,7 @@ import pytest
 
 
 def test_inference():
-    model = GPT4All(model_name='orca-mini-3b.ggmlv3.q4_0.bin')
+    model = GPT4All(model_name='orca-mini-3b-gguf2-q4_0.gguf')
     output_1 = model.generate('hello', top_k=1)
 
     with model.chat_session():
@@ -28,12 +28,8 @@ def test_inference():
     assert len(tokens) > 0
 
     with model.chat_session():
-        tokens = list(model.generate(prompt='hello', top_k=1, streaming=True))
-        model.current_chat_session.append({'role': 'assistant', 'content': ''.join(tokens)})
-
-        tokens = list(model.generate(prompt='write me a poem about dogs', top_k=1, streaming=True))
-        model.current_chat_session.append({'role': 'assistant', 'content': ''.join(tokens)})
-
+        model.generate(prompt='hello', top_k=1, streaming=True)
+        model.generate(prompt='write me a poem about dogs', top_k=1, streaming=True)
         print(model.current_chat_session)
 
 
@@ -47,49 +43,44 @@ def do_long_input(model):
 
 
 def test_inference_long_orca_3b():
-    model = GPT4All(model_name="orca-mini-3b.ggmlv3.q4_0.bin")
+    model = GPT4All(model_name="orca-mini-3b-gguf2-q4_0.gguf")
     do_long_input(model)
 
 
 def test_inference_long_falcon():
-    model = GPT4All(model_name='ggml-model-gpt4all-falcon-q4_0.bin')
+    model = GPT4All(model_name='gpt4all-falcon-q4_0.gguf')
     do_long_input(model)
 
 
 def test_inference_long_llama_7b():
-    model = GPT4All(model_name="orca-mini-7b.ggmlv3.q4_0.bin")
+    model = GPT4All(model_name="mistral-7b-openorca.Q4_0.gguf")
     do_long_input(model)
 
 
 def test_inference_long_llama_13b():
-    model = GPT4All(model_name='ggml-nous-hermes-13b.ggmlv3.q4_0.bin')
+    model = GPT4All(model_name='nous-hermes-llama2-13b.Q4_0.gguf')
     do_long_input(model)
 
 
 def test_inference_long_mpt():
-    model = GPT4All(model_name='ggml-mpt-7b-chat.bin')
+    model = GPT4All(model_name='mpt-7b-chat-q4_0.gguf')
     do_long_input(model)
 
 
 def test_inference_long_replit():
-    model = GPT4All(model_name='ggml-replit-code-v1-3b.bin')
-    do_long_input(model)
-
-
-def test_inference_long_groovy():
-    model = GPT4All(model_name='ggml-gpt4all-j-v1.3-groovy.bin')
+    model = GPT4All(model_name='replit-code-v1_5-3b-q4_0.gguf')
     do_long_input(model)
 
 
 def test_inference_hparams():
-    model = GPT4All(model_name='orca-mini-3b.ggmlv3.q4_0.bin')
+    model = GPT4All(model_name='orca-mini-3b-gguf2-q4_0.gguf')
 
     output = model.generate("The capital of france is ", max_tokens=3)
     assert 'Paris' in output
 
 
 def test_inference_falcon():
-    model = GPT4All(model_name='ggml-model-gpt4all-falcon-q4_0.bin')
+    model = GPT4All(model_name='gpt4all-falcon-q4_0.gguf')
     prompt = 'hello'
     output = model.generate(prompt)
     assert isinstance(output, str)
@@ -97,7 +88,7 @@ def test_inference_falcon():
 
 
 def test_inference_mpt():
-    model = GPT4All(model_name='ggml-mpt-7b-chat.bin')
+    model = GPT4All(model_name='mpt-7b-chat-q4_0.gguf')
     prompt = 'hello'
     output = model.generate(prompt)
     assert isinstance(output, str)
@@ -120,13 +111,13 @@ def test_empty_embedding():
         output = embedder.embed(text)
 
 def test_download_model(tmp_path: Path):
-    import gpt4all.gpt4all
-    old_default_dir = gpt4all.gpt4all.DEFAULT_MODEL_DIRECTORY
-    gpt4all.gpt4all.DEFAULT_MODEL_DIRECTORY = tmp_path  # temporary pytest directory to ensure a download happens
+    from gpt4all import gpt4all
+    old_default_dir = gpt4all.DEFAULT_MODEL_DIRECTORY
+    gpt4all.DEFAULT_MODEL_DIRECTORY = tmp_path  # temporary pytest directory to ensure a download happens
     try:
         model = GPT4All(model_name='ggml-all-MiniLM-L6-v2-f16.bin')
         model_path = tmp_path / model.config['filename']
         assert model_path.absolute() == Path(model.config['path']).absolute()
         assert model_path.stat().st_size == int(model.config['filesize'])
     finally:
-        gpt4all.gpt4all.DEFAULT_MODEL_DIRECTORY = old_default_dir
+        gpt4all.DEFAULT_MODEL_DIRECTORY = old_default_dir

gpt4all-bindings/python/mkdocs.yml

@@ -14,10 +14,8 @@ nav:
       - 'GPT4All in Python':
         - 'Generation': 'gpt4all_python.md'
         - 'Embedding': 'gpt4all_python_embedding.md'
-      - 'GPT4ALL in NodeJs': 'gpt4all_typescript.md'
+      - 'GPT4ALL in NodeJs': 'gpt4all_nodejs.md'
       - 'gpt4all_cli.md'
-#    - 'Tutorials':
-#      - 'gpt4all_modal.md'
     - 'Wiki':
       - 'gpt4all_faq.md'
 
@@ -44,8 +42,8 @@ markdown_extensions:
   - pymdownx.tabbed:
       alternate_style: true
   - pymdownx.emoji:
-      emoji_index: !!python/name:materialx.emoji.twemoji
-      emoji_generator: !!python/name:materialx.emoji.to_svg
+      emoji_index: !!python/name:material.extensions.emoji.twemoji
+      emoji_generator: !!python/name:material.extensions.emoji.to_svg
       options:
         custom_icons:
           - docs/overrides/.icons

gpt4all-bindings/python/setup.py

@@ -1,12 +1,13 @@
 from setuptools import setup, find_packages
 import os
+import pathlib
 import platform
 import shutil
 
 package_name = "gpt4all"
 
 # Define the location of your prebuilt C library files
-SRC_CLIB_DIRECtORY = os.path.join("..", "..", "gpt4all-backend")
+SRC_CLIB_DIRECTORY = os.path.join("..", "..", "gpt4all-backend")
 SRC_CLIB_BUILD_DIRECTORY = os.path.join("..", "..", "gpt4all-backend", "build") 
 
 LIB_NAME = "llmodel"
@@ -55,17 +56,29 @@ def copy_prebuilt_C_lib(src_dir, dest_dir, dest_build_dir):
 
 # NOTE: You must provide correct path to the prebuilt llmodel C library. 
 # Specifically, the llmodel.h and C shared library are needed.
-copy_prebuilt_C_lib(SRC_CLIB_DIRECtORY,
+copy_prebuilt_C_lib(SRC_CLIB_DIRECTORY,
                     DEST_CLIB_DIRECTORY,
                     DEST_CLIB_BUILD_DIRECTORY)
 
+
+def get_long_description():
+    with open(pathlib.Path(__file__).parent / "README.md", encoding="utf-8") as fp:
+        return fp.read()
+
+
 setup(
     name=package_name,
-    version="2.0.0rc2",
+    version="2.3.2",
     description="Python bindings for GPT4All",
+    long_description=get_long_description(),
+    long_description_content_type="text/markdown",
     author="Nomic and the Open Source Community",
     author_email="support@nomic.ai",
-    url="https://pypi.org/project/gpt4all/",
+    url="https://gpt4all.io/",
+    project_urls={
+        "Documentation": "https://docs.gpt4all.io/gpt4all_python.html",
+        "Source code": "https://github.com/nomic-ai/gpt4all/tree/main/gpt4all-bindings/python",
+    },
     classifiers = [
         "Programming Language :: Python :: 3",
         "License :: OSI Approved :: MIT License",
@@ -73,7 +86,12 @@ setup(
     ],
     python_requires='>=3.8',
     packages=find_packages(),
-    install_requires=['requests', 'tqdm'],
+    install_requires=[
+        'requests',
+        'tqdm',
+        'importlib_resources; python_version < "3.9"',
+        'typing-extensions>=4.3.0; python_version >= "3.9" and python_version < "3.11"',
+    ],
     extras_require={
         'dev': [
             'pytest',
@@ -85,7 +103,8 @@ setup(
             'mkdocstrings[python]',
             'mkdocs-jupyter',
             'black',
-            'isort'
+            'isort',
+            'typing-extensions>=3.10',
         ]
     },
     package_data={'llmodel': [os.path.join(DEST_CLIB_DIRECTORY, "*")]},

gpt4all-bindings/typescript/.gitignore

@@ -8,3 +8,4 @@ prebuilds/
 !.yarn/sdks
 !.yarn/versions
 runtimes/
+compile_flags.txt

gpt4all-bindings/typescript/.yarnrc.yml

@@ -0,0 +1 @@
+nodeLinker: node-modules