mirror of
https://github.com/nomic-ai/gpt4all.git
synced 2025-08-07 19:13:28 +00:00
python: various fixes for GPT4All and Embed4All (#2130)
Key changes: * honor empty system prompt argument * current_chat_session is now read-only and defaults to None * deprecate fallback prompt template for unknown models * fix mistakes from #2086 Signed-off-by: Jared Van Bortel <jared@nomic.ai>
This commit is contained in:
Jared Van Bortel
GitHub
parent
53f109f519
commit
255568fb9a
@ -10,6 +10,7 @@
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#include <iomanip>
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#include <iostream>
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#include <map>
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#include <numeric>
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#include <random>
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#include <sstream>
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#include <stdexcept>
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@ -345,7 +346,7 @@ bool LLamaModel::loadModel(const std::string &modelPath, int n_ctx, int ngl)
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d_ptr->ctx_params.n_threads = d_ptr->n_threads;
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d_ptr->ctx_params.n_threads_batch = d_ptr->n_threads;
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if (m_supportsEmbedding)
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if (isEmbedding)
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d_ptr->ctx_params.embeddings = true;
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d_ptr->ctx = llama_new_context_with_model(d_ptr->model, d_ptr->ctx_params);
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@ -612,22 +613,22 @@ struct EmbModelGroup {
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std::vector<const char *> names;
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};
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static const EmbModelSpec NOPREFIX_SPEC {nullptr, nullptr};
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static const EmbModelSpec NOPREFIX_SPEC {"", ""};
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static const EmbModelSpec NOMIC_SPEC {"search_document", "search_query", {"clustering", "classification"}};
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static const EmbModelSpec E5_SPEC {"passage", "query"};
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static const EmbModelSpec NOMIC_1_5_SPEC {
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"search_document", "search_query", {"clustering", "classification"}, true, "[768, 512, 384, 256, 128]"
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"search_document", "search_query", {"clustering", "classification"}, true, "[768, 512, 384, 256, 128]",
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};
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static const EmbModelSpec LLM_EMBEDDER_SPEC {
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"Represent this document for retrieval",
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"Represent this query for retrieving relevant documents",
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};
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static const EmbModelSpec BGE_SPEC {
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nullptr, "Represent this sentence for searching relevant passages",
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"", "Represent this sentence for searching relevant passages",
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};
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static const EmbModelSpec E5_MISTRAL_SPEC {
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nullptr, "Instruct: Given a query, retrieve relevant passages that answer the query\nQuery",
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"", "Instruct: Given a query, retrieve relevant passages that answer the query\nQuery",
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};
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static const EmbModelGroup EMBEDDING_MODEL_SPECS[] {
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@ -738,18 +739,20 @@ void LLamaModel::embedInternal(
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const llama_token bos_token = llama_token_bos(d_ptr->model);
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const llama_token eos_token = llama_token_eos(d_ptr->model);
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assert(shouldAddBOS());
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bool addEOS = llama_vocab_type(d_ptr->model) == LLAMA_VOCAB_TYPE_WPM;
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bool useBOS = shouldAddBOS();
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bool useEOS = llama_vocab_type(d_ptr->model) == LLAMA_VOCAB_TYPE_WPM;
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// no EOS, optional BOS
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auto tokenize = [this, addEOS](std::string text, TokenString &tokens, bool addBOS) {
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if (!text.empty() && text[0] != ' ')
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auto tokenize = [this, useBOS, useEOS, eos_token](std::string text, TokenString &tokens, bool wantBOS) {
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if (!text.empty() && text[0] != ' ') {
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text = ' ' + text; // normalize for SPM - our fork of llama.cpp doesn't add a space prefix
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}
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wantBOS &= useBOS;
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tokens.resize(text.length()+4);
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int32_t n_tokens = llama_tokenize(d_ptr->model, text.c_str(), text.length(), tokens.data(), tokens.size(), addBOS, false);
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assert(addEOS == (eos_token != -1 && tokens[n_tokens - 1] == eos_token));
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tokens.resize(n_tokens - addEOS); // erase EOS/SEP
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int32_t n_tokens = llama_tokenize(d_ptr->model, text.c_str(), text.length(), tokens.data(), tokens.size(), wantBOS, false);
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assert(useEOS == (eos_token != -1 && tokens[n_tokens - 1] == eos_token));
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tokens.resize(n_tokens - useEOS); // erase EOS/SEP
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};
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// tokenize the texts
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@ -784,7 +787,7 @@ void LLamaModel::embedInternal(
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}
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const uint32_t n_batch = llama_n_batch(d_ptr->ctx);
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const uint32_t max_len = n_batch - (prefixTokens.size() + addEOS); // minus BOS/CLS and EOS/SEP
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const uint32_t max_len = n_batch - (prefixTokens.size() + useEOS); // minus BOS/CLS and EOS/SEP
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if (chunkOverlap >= max_len) {
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throw std::logic_error("max chunk length of " + std::to_string(max_len) + " is smaller than overlap of " +
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std::to_string(chunkOverlap) + " tokens");
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@ -1,7 +1,6 @@
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from __future__ import annotations
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import ctypes
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import logging
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import os
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import platform
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import re
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@ -17,8 +16,6 @@ if sys.version_info >= (3, 9):
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else:
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import importlib_resources
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logger: logging.Logger = logging.getLogger(__name__)
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# TODO: provide a config file to make this more robust
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MODEL_LIB_PATH = importlib_resources.files("gpt4all") / "llmodel_DO_NOT_MODIFY" / "build"
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@ -130,7 +127,7 @@ llmodel.llmodel_set_implementation_search_path.restype = None
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llmodel.llmodel_threadCount.argtypes = [ctypes.c_void_p]
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llmodel.llmodel_threadCount.restype = ctypes.c_int32
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llmodel.llmodel_set_implementation_search_path(str(MODEL_LIB_PATH).replace("\\", r"\\").encode())
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llmodel.llmodel_set_implementation_search_path(str(MODEL_LIB_PATH).encode())
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llmodel.llmodel_available_gpu_devices.argtypes = [ctypes.c_void_p, ctypes.c_size_t, ctypes.POINTER(ctypes.c_int32)]
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llmodel.llmodel_available_gpu_devices.restype = ctypes.POINTER(LLModelGPUDevice)
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@ -323,7 +320,7 @@ class LLModel:
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ctypes.byref(error),
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)
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if embedding_ptr.value is None:
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if not embedding_ptr:
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msg = "(unknown error)" if error.value is None else error.value.decode()
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raise RuntimeError(f'Failed to generate embeddings: {msg}')
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@ -372,13 +369,6 @@ class LLModel:
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self.buffer.clear()
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self.buff_expecting_cont_bytes = 0
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logger.info(
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"LLModel.prompt_model -- prompt:\n"
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+ "%s\n"
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+ "===/LLModel.prompt_model -- prompt/===",
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prompt,
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)
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self._set_context(
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n_predict=n_predict,
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top_k=top_k,
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@ -20,12 +20,9 @@ from urllib3.exceptions import IncompleteRead, ProtocolError
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from . import _pyllmodel
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# TODO: move to config
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DEFAULT_MODEL_DIRECTORY = os.path.join(str(Path.home()), ".cache", "gpt4all").replace("\\", "\\\\")
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DEFAULT_MODEL_DIRECTORY = Path.home() / ".cache" / "gpt4all"
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DEFAULT_MODEL_CONFIG = {
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"systemPrompt": "",
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"promptTemplate": "### Human: \n{0}\n\n### Assistant:\n",
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}
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DEFAULT_PROMPT_TEMPLATE = "### Human:\n{0}\n\n### Assistant:\n"
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ConfigType = Dict[str, str]
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MessageType = Dict[str, str]
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@ -34,18 +31,19 @@ MessageType = Dict[str, str]
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class Embed4All:
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"""
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Python class that handles embeddings for GPT4All.
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Args:
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model_name: The name of the embedding model to use. Defaults to `all-MiniLM-L6-v2.gguf2.f16.gguf`.
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All other arguments are passed to the GPT4All constructor. See its documentation for more info.
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"""
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MIN_DIMENSIONALITY = 64
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def __init__(self, model_name: Optional[str] = None, n_threads: Optional[int] = None, **kwargs):
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"""
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Constructor
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Args:
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n_threads: number of CPU threads used by GPT4All. Default is None, then the number of threads are determined automatically.
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"""
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self.gpt4all = GPT4All(model_name or 'all-MiniLM-L6-v2-f16.gguf', n_threads=n_threads, **kwargs)
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def __init__(self, model_name: Optional[str] = None, **kwargs):
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if model_name is None:
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model_name = 'all-MiniLM-L6-v2.gguf2.f16.gguf'
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self.gpt4all = GPT4All(model_name, **kwargs)
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@overload
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def embed(
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@ -58,7 +56,7 @@ class Embed4All:
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atlas: bool = ...,
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) -> list[list[float]]: ...
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def embed(self, text, prefix=None, dimensionality=None, long_text_mode="truncate", atlas=False):
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def embed(self, text, prefix=None, dimensionality=None, long_text_mode="mean", atlas=False):
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"""
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Generate one or more embeddings.
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@ -94,22 +92,6 @@ class Embed4All:
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class GPT4All:
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"""
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Python class that handles instantiation, downloading, generation and chat with GPT4All models.
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"""
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def __init__(
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self,
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model_name: str,
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model_path: Optional[Union[str, os.PathLike[str]]] = None,
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model_type: Optional[str] = None,
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allow_download: bool = True,
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n_threads: Optional[int] = None,
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device: Optional[str] = "cpu",
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n_ctx: int = 2048,
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ngl: int = 100,
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verbose: bool = False,
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):
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"""
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Constructor
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Args:
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model_name: Name of GPT4All or custom model. Including ".gguf" file extension is optional but encouraged.
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@ -131,6 +113,19 @@ class GPT4All:
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ngl: Number of GPU layers to use (Vulkan)
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verbose: If True, print debug messages.
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"""
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def __init__(
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self,
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model_name: str,
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model_path: Optional[Union[str, os.PathLike[str]]] = None,
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model_type: Optional[str] = None,
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allow_download: bool = True,
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n_threads: Optional[int] = None,
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device: Optional[str] = "cpu",
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n_ctx: int = 2048,
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ngl: int = 100,
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verbose: bool = False,
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):
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self.model_type = model_type
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# Retrieve model and download if allowed
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self.config: ConfigType = self.retrieve_model(model_name, model_path=model_path, allow_download=allow_download, verbose=verbose)
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@ -142,10 +137,13 @@ class GPT4All:
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if n_threads is not None:
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self.model.set_thread_count(n_threads)
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self._is_chat_session_activated: bool = False
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self.current_chat_session: List[MessageType] = empty_chat_session()
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self._history: list[MessageType] | None = None
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self._current_prompt_template: str = "{0}"
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@property
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def current_chat_session(self) -> list[MessageType] | None:
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return self._history
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@staticmethod
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def list_models() -> List[ConfigType]:
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"""
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@ -159,8 +157,9 @@ class GPT4All:
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raise ValueError(f'Request failed: HTTP {resp.status_code} {resp.reason}')
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return resp.json()
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@staticmethod
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@classmethod
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def retrieve_model(
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cls,
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model_name: str,
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model_path: Optional[Union[str, os.PathLike[str]]] = None,
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allow_download: bool = True,
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@ -183,58 +182,51 @@ class GPT4All:
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model_filename = append_extension_if_missing(model_name)
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# get the config for the model
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config: ConfigType = DEFAULT_MODEL_CONFIG
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config: ConfigType = {}
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if allow_download:
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available_models = GPT4All.list_models()
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available_models = cls.list_models()
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for m in available_models:
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if model_filename == m["filename"]:
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config.update(m)
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config["systemPrompt"] = config["systemPrompt"].strip()
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tmpl = m.get("promptTemplate", DEFAULT_PROMPT_TEMPLATE)
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# change to Python-style formatting
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config["promptTemplate"] = config["promptTemplate"].replace("%1", "{0}", 1).replace("%2", "{1}", 1)
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m["promptTemplate"] = tmpl.replace("%1", "{0}", 1).replace("%2", "{1}", 1)
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config.update(m)
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break
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# Validate download directory
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if model_path is None:
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try:
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os.makedirs(DEFAULT_MODEL_DIRECTORY, exist_ok=True)
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except OSError as exc:
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raise ValueError(
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f"Failed to create model download directory at {DEFAULT_MODEL_DIRECTORY}: {exc}. "
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"Please specify model_path."
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)
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except OSError as e:
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raise RuntimeError("Failed to create model download directory") from e
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model_path = DEFAULT_MODEL_DIRECTORY
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else:
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model_path = str(model_path).replace("\\", "\\\\")
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model_path = Path(model_path)
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if not os.path.exists(model_path):
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raise ValueError(f"Invalid model directory: {model_path}")
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if not model_path.exists():
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raise FileNotFoundError(f"Model directory does not exist: {model_path!r}")
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model_dest = os.path.join(model_path, model_filename).replace("\\", "\\\\")
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if os.path.exists(model_dest):
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config.pop("url", None)
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config["path"] = model_dest
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model_dest = model_path / model_filename
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if model_dest.exists():
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config["path"] = str(model_dest)
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if verbose:
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print("Found model file at", model_dest, file=sys.stderr)
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# If model file does not exist, download
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print(f"Found model file at {str(model_dest)!r}", file=sys.stderr)
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elif allow_download:
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url = config.pop("url", None)
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config["path"] = GPT4All.download_model(model_filename, model_path, verbose=verbose, url=url)
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# If model file does not exist, download
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config["path"] = str(cls.download_model(model_filename, model_path, verbose=verbose, url=config.get("url")))
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else:
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raise ValueError("Failed to retrieve model")
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raise FileNotFoundError(f"Model file does not exist: {model_dest!r}")
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return config
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@staticmethod
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def download_model(
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model_filename: str,
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model_path: Union[str, os.PathLike[str]],
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model_path: str | os.PathLike[str],
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verbose: bool = True,
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url: Optional[str] = None,
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) -> str:
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) -> str | os.PathLike[str]:
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"""
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Download model from https://gpt4all.io.
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@ -248,21 +240,17 @@ class GPT4All:
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Model file destination.
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"""
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def get_download_url(model_filename):
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if url:
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return url
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return f"https://gpt4all.io/models/gguf/{model_filename}"
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# Download model
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download_path = os.path.join(model_path, model_filename).replace("\\", "\\\\")
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download_url = get_download_url(model_filename)
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download_path = Path(model_path) / model_filename
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if url is None:
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url = f"https://gpt4all.io/models/gguf/{model_filename}"
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def make_request(offset=None):
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headers = {}
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if offset:
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print(f"\nDownload interrupted, resuming from byte position {offset}", file=sys.stderr)
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headers['Range'] = f'bytes={offset}-' # resume incomplete response
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response = requests.get(download_url, stream=True, headers=headers)
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response = requests.get(url, stream=True, headers=headers)
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if response.status_code not in (200, 206):
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raise ValueError(f'Request failed: HTTP {response.status_code} {response.reason}')
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if offset and (response.status_code != 206 or str(offset) not in response.headers.get('Content-Range', '')):
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@ -311,7 +299,7 @@ class GPT4All:
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time.sleep(2) # Sleep for a little bit so Windows can remove file lock
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if verbose:
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print("Model downloaded at:", download_path, file=sys.stderr)
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print(f"Model downloaded to {str(download_path)!r}", file=sys.stderr)
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return download_path
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def generate(
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@ -350,10 +338,6 @@ class GPT4All:
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Either the entire completion or a generator that yields the completion token by token.
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"""
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if re.search(r"%1(?![0-9])", self._current_prompt_template):
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raise ValueError("Prompt template containing a literal '%1' is not supported. For a prompt "
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"placeholder, please use '{0}' instead.")
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# Preparing the model request
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generate_kwargs: Dict[str, Any] = dict(
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temp=temp,
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@ -366,17 +350,17 @@ class GPT4All:
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n_predict=n_predict if n_predict is not None else max_tokens,
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)
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if self._is_chat_session_activated:
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if self._history is not None:
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# check if there is only one message, i.e. system prompt:
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reset = len(self.current_chat_session) == 1
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reset = len(self._history) == 1
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generate_kwargs["reset_context"] = reset
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self.current_chat_session.append({"role": "user", "content": prompt})
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self._history.append({"role": "user", "content": prompt})
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fct_func = self._format_chat_prompt_template.__func__ # type: ignore[attr-defined]
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if fct_func is GPT4All._format_chat_prompt_template:
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if reset:
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# ingest system prompt
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self.model.prompt_model(self.current_chat_session[0]["content"], "%1",
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self.model.prompt_model(self._history[0]["content"], "%1",
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_pyllmodel.empty_response_callback,
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n_batch=n_batch, n_predict=0, special=True)
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prompt_template = self._current_prompt_template.format("%1", "%2")
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@ -387,8 +371,8 @@ class GPT4All:
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)
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# special tokens won't be processed
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prompt = self._format_chat_prompt_template(
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self.current_chat_session[-1:],
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self.current_chat_session[0]["content"] if reset else "",
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self._history[-1:],
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self._history[0]["content"] if reset else "",
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)
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prompt_template = "%1"
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else:
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@ -399,11 +383,11 @@ class GPT4All:
|
||||
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,
|
||||
@ -439,8 +423,8 @@ 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.
|
||||
@ -449,16 +433,27 @@ 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(
|
||||
@ -496,10 +491,6 @@ class GPT4All:
|
||||
return full_prompt
|
||||
|
||||
|
||||
def empty_chat_session(system_prompt: str = "") -> List[MessageType]:
|
||||
return [{"role": "system", "content": system_prompt}]
|
||||
|
||||
|
||||
def append_extension_if_missing(model_name):
|
||||
if not model_name.endswith((".bin", ".gguf")):
|
||||
model_name += ".gguf"
|
||||
|
||||
@ -115,13 +115,13 @@ 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 = str(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
|
||||
|
||||
@ -24,7 +24,7 @@ const DEFAULT_LIBRARIES_DIRECTORY = librarySearchPaths.join(";");
|
||||
|
||||
const DEFAULT_MODEL_CONFIG = {
|
||||
systemPrompt: "",
|
||||
promptTemplate: "### Human: \n%1\n### Assistant:\n",
|
||||
promptTemplate: "### Human:\n%1\n\n### Assistant:\n",
|
||||
}
|
||||
|
||||
const DEFAULT_MODEL_LIST_URL = "https://gpt4all.io/models/models2.json";
|
||||
|
||||
@ -29,7 +29,7 @@
|
||||
"description": "<strong>Strong overall fast chat model</strong><br><ul><li>Fast responses</li><li>Chat based model</li><li>Trained by Mistral AI<li>Finetuned on OpenOrca dataset curated via <a href=\"https://atlas.nomic.ai/\">Nomic Atlas</a><li>Licensed for commercial use</ul>",
|
||||
"url": "https://gpt4all.io/models/gguf/mistral-7b-openorca.gguf2.Q4_0.gguf",
|
||||
"promptTemplate": "<|im_start|>user\n%1<|im_end|>\n<|im_start|>assistant\n%2<|im_end|>\n",
|
||||
"systemPrompt": "<|im_start|>system\nYou are MistralOrca, a large language model trained by Alignment Lab AI. For multi-step problems, write out your reasoning for each step.\n<|im_end|>"
|
||||
"systemPrompt": "<|im_start|>system\nYou are MistralOrca, a large language model trained by Alignment Lab AI. For multi-step problems, write out your reasoning for each step.\n<|im_end|>\n"
|
||||
},
|
||||
{
|
||||
"order": "c",
|
||||
@ -154,7 +154,7 @@
|
||||
"description": "<strong>Good model with novel architecture</strong><br><ul><li>Fast responses<li>Chat based<li>Trained by Mosaic ML<li>Cannot be used commercially</ul>",
|
||||
"url": "https://gpt4all.io/models/gguf/mpt-7b-chat-newbpe-q4_0.gguf",
|
||||
"promptTemplate": "<|im_start|>user\n%1<|im_end|>\n<|im_start|>assistant\n%2<|im_end|>\n",
|
||||
"systemPrompt": "<|im_start|>system\n- You are a helpful assistant chatbot trained by MosaicML.\n- You answer questions.\n- You are excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user.\n- You are more than just an information source, you are also able to write poetry, short stories, and make jokes.<|im_end|>"
|
||||
"systemPrompt": "<|im_start|>system\n- You are a helpful assistant chatbot trained by MosaicML.\n- You answer questions.\n- You are excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user.\n- You are more than just an information source, you are also able to write poetry, short stories, and make jokes.<|im_end|>\n"
|
||||
},
|
||||
{
|
||||
"order": "j",
|
||||
@ -170,7 +170,7 @@
|
||||
"description": "<strong>Good model with novel architecture</strong><br><ul><li>Fast responses<li>Chat based<li>Trained by Mosaic ML<li>Cannot be used commercially</ul>",
|
||||
"url": "https://gpt4all.io/models/gguf/mpt-7b-chat.gguf4.Q4_0.gguf",
|
||||
"promptTemplate": "<|im_start|>user\n%1<|im_end|>\n<|im_start|>assistant\n%2<|im_end|>\n",
|
||||
"systemPrompt": "<|im_start|>system\n- You are a helpful assistant chatbot trained by MosaicML.\n- You answer questions.\n- You are excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user.\n- You are more than just an information source, you are also able to write poetry, short stories, and make jokes.<|im_end|>"
|
||||
"systemPrompt": "<|im_start|>system\n- You are a helpful assistant chatbot trained by MosaicML.\n- You answer questions.\n- You are excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user.\n- You are more than just an information source, you are also able to write poetry, short stories, and make jokes.<|im_end|>\n"
|
||||
},
|
||||
{
|
||||
"order": "k",
|
||||
|
||||
Loading…
Reference in New Issue
Block a user