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https://github.com/csunny/DB-GPT.git
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commit
ad8e0e353a
@ -37,6 +37,7 @@ LLM_MODEL_CONFIG = {
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"guanaco-33b-merged": os.path.join(MODEL_PATH, "guanaco-33b-merged"),
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"falcon-40b": os.path.join(MODEL_PATH, "falcon-40b"),
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"gorilla-7b": os.path.join(MODEL_PATH, "gorilla-7b"),
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"gptj-6b": os.path.join(MODEL_PATH, "ggml-gpt4all-j-v1.3-groovy.bin"),
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"proxyllm": "proxyllm",
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}
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@ -2,6 +2,7 @@
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# -*- coding: utf-8 -*-
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import torch
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import os
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from typing import List
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from functools import cache
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from transformers import (
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@ -185,18 +186,26 @@ class RWKV4LLMAdapter(BaseLLMAdaper):
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class GPT4AllAdapter(BaseLLMAdaper):
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"""A light version for someone who want practise LLM use laptop."""
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"""
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A light version for someone who want practise LLM use laptop.
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All model names see: https://gpt4all.io/models/models.json
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"""
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def match(self, model_path: str):
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return "gpt4all" in model_path
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def loader(self, model_path: str, from_pretrained_kwargs: dict):
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# TODO
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pass
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import gpt4all
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if model_path is None and from_pretrained_kwargs.get("model_name") is None:
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model = gpt4all.GPT4All("ggml-gpt4all-j-v1.3-groovy")
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else:
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path, file = os.path.split(model_path)
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model = gpt4all.GPT4All(model_path=path, model_name=file)
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return model, None
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class ProxyllmAdapter(BaseLLMAdaper):
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"""The model adapter for local proxy"""
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def match(self, model_path: str):
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@ -211,6 +220,7 @@ register_llm_model_adapters(ChatGLMAdapater)
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register_llm_model_adapters(GuanacoAdapter)
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register_llm_model_adapters(FalconAdapater)
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register_llm_model_adapters(GorillaAdapter)
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register_llm_model_adapters(GPT4AllAdapter)
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# TODO Default support vicuna, other model need to tests and Evaluate
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# just for test, remove this later
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23
pilot/model/llm_out/gpt4all_llm.py
Normal file
23
pilot/model/llm_out/gpt4all_llm.py
Normal file
@ -0,0 +1,23 @@
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#!/usr/bin/env python3
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# -*- coding:utf-8 -*-
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import threading
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import sys
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import time
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def gpt4all_generate_stream(model, tokenizer, params, device, max_position_embeddings):
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stop = params.get("stop", "###")
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prompt = params["prompt"]
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role, query = prompt.split(stop)[1].split(":")
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print(f"gpt4all, role: {role}, query: {query}")
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def worker():
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model.generate(prompt=query, streaming=True)
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t = threading.Thread(target=worker)
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t.start()
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while t.is_alive():
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yield sys.stdout.output
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time.sleep(0.01)
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t.join()
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@ -51,7 +51,7 @@ class BaseOutputParser(ABC):
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""" TODO Multi mode output handler, rewrite this for multi model, use adapter mode.
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"""
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if data["error_code"] == 0:
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if data.get("error_code", 0) == 0:
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if "vicuna" in CFG.LLM_MODEL:
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# output = data["text"][skip_echo_len + 11:].strip()
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output = data["text"][skip_echo_len:].strip()
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@ -37,7 +37,6 @@ def get_llm_chat_adapter(model_path: str) -> BaseChatAdpter:
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class VicunaChatAdapter(BaseChatAdpter):
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"""Model chat Adapter for vicuna"""
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def match(self, model_path: str):
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@ -60,7 +59,6 @@ class ChatGLMChatAdapter(BaseChatAdpter):
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class CodeT5ChatAdapter(BaseChatAdpter):
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"""Model chat adapter for CodeT5"""
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def match(self, model_path: str):
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@ -72,7 +70,6 @@ class CodeT5ChatAdapter(BaseChatAdpter):
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class CodeGenChatAdapter(BaseChatAdpter):
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"""Model chat adapter for CodeGen"""
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def match(self, model_path: str):
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@ -127,11 +124,22 @@ class GorillaChatAdapter(BaseChatAdpter):
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return generate_stream
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class GPT4AllChatAdapter(BaseChatAdpter):
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def match(self, model_path: str):
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return "gpt4all" in model_path
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def get_generate_stream_func(self):
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from pilot.model.llm_out.gpt4all_llm import gpt4all_generate_stream
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return gpt4all_generate_stream
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register_llm_model_chat_adapter(VicunaChatAdapter)
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register_llm_model_chat_adapter(ChatGLMChatAdapter)
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register_llm_model_chat_adapter(GuanacoChatAdapter)
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register_llm_model_chat_adapter(FalconChatAdapter)
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register_llm_model_chat_adapter(GorillaChatAdapter)
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register_llm_model_chat_adapter(GPT4AllChatAdapter)
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# Proxy model for test and develop, it's cheap for us now.
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register_llm_model_chat_adapter(ProxyllmChatAdapter)
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@ -39,9 +39,13 @@ class ModelWorker:
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)
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if not isinstance(self.model, str):
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if hasattr(self.model.config, "max_sequence_length"):
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if hasattr(self.model, "config") and hasattr(
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self.model.config, "max_sequence_length"
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):
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self.context_len = self.model.config.max_sequence_length
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elif hasattr(self.model.config, "max_position_embeddings"):
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elif hasattr(self.model, "config") and hasattr(
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self.model.config, "max_position_embeddings"
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):
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self.context_len = self.model.config.max_position_embeddings
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else:
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@ -69,7 +73,10 @@ class ModelWorker:
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for output in self.generate_stream_func(
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self.model, self.tokenizer, params, DEVICE, CFG.MAX_POSITION_EMBEDDINGS
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):
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print("output: ", output)
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# Please do not open the output in production!
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# The gpt4all thread shares stdout with the parent process,
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# and opening it may affect the frontend output.
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# print("output: ", output)
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ret = {
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"text": output,
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"error_code": 0,
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@ -49,6 +49,7 @@ llama-index==0.5.27
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pymysql
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unstructured==0.6.3
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grpcio==1.47.5
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gpt4all==0.3.0
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auto-gpt-plugin-template
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pymdown-extensions
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