294df6c2d5
* ✨ feat(request_llms and config.py): ChatGLM4 Deployment Add support for local deployment of ChatGLM4 model * 🦄 refactor(bridge_chatglm3.py): ChatGLM3 model path Added ChatGLM3 path customization (in config.py). Removed useless quantization model options that have been annotated --------- Co-authored-by: MarkDeia <17290550+MarkDeia@users.noreply.github.com>
98 lines
3.9 KiB
Python
98 lines
3.9 KiB
Python
model_name = "ChatGLM3"
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cmd_to_install = "`pip install -r request_llms/requirements_chatglm.txt`"
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from toolbox import get_conf, ProxyNetworkActivate
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from .local_llm_class import LocalLLMHandle, get_local_llm_predict_fns
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# ------------------------------------------------------------------------------------------------------------------------
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# 🔌💻 Local Model
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# ------------------------------------------------------------------------------------------------------------------------
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class GetGLM3Handle(LocalLLMHandle):
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def load_model_info(self):
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# 🏃♂️🏃♂️🏃♂️ 子进程执行
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self.model_name = model_name
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self.cmd_to_install = cmd_to_install
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def load_model_and_tokenizer(self):
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# 🏃♂️🏃♂️🏃♂️ 子进程执行
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from transformers import AutoModel, AutoTokenizer, BitsAndBytesConfig
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import os, glob
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import os
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import platform
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LOCAL_MODEL_PATH, LOCAL_MODEL_QUANT, device = get_conf("CHATGLM_LOCAL_MODEL_PATH", "LOCAL_MODEL_QUANT", "LOCAL_MODEL_DEVICE")
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model_path = LOCAL_MODEL_PATH
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with ProxyNetworkActivate("Download_LLM"):
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chatglm_tokenizer = AutoTokenizer.from_pretrained(
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model_path, trust_remote_code=True
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)
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if device == "cpu":
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chatglm_model = AutoModel.from_pretrained(
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model_path,
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trust_remote_code=True,
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device="cpu",
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).float()
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elif LOCAL_MODEL_QUANT == "INT4": # INT4
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chatglm_model = AutoModel.from_pretrained(
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pretrained_model_name_or_path=model_path,
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trust_remote_code=True,
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quantization_config=BitsAndBytesConfig(load_in_4bit=True),
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)
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elif LOCAL_MODEL_QUANT == "INT8": # INT8
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chatglm_model = AutoModel.from_pretrained(
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pretrained_model_name_or_path=model_path,
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trust_remote_code=True,
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quantization_config=BitsAndBytesConfig(load_in_8bit=True),
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)
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else:
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chatglm_model = AutoModel.from_pretrained(
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pretrained_model_name_or_path=model_path,
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trust_remote_code=True,
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device="cuda",
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)
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chatglm_model = chatglm_model.eval()
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self._model = chatglm_model
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self._tokenizer = chatglm_tokenizer
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return self._model, self._tokenizer
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def llm_stream_generator(self, **kwargs):
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# 🏃♂️🏃♂️🏃♂️ 子进程执行
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def adaptor(kwargs):
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query = kwargs["query"]
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max_length = kwargs["max_length"]
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top_p = kwargs["top_p"]
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temperature = kwargs["temperature"]
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history = kwargs["history"]
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return query, max_length, top_p, temperature, history
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query, max_length, top_p, temperature, history = adaptor(kwargs)
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for response, history in self._model.stream_chat(
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self._tokenizer,
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query,
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history,
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max_length=max_length,
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top_p=top_p,
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temperature=temperature,
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):
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yield response
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def try_to_import_special_deps(self, **kwargs):
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# import something that will raise error if the user does not install requirement_*.txt
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# 🏃♂️🏃♂️🏃♂️ 主进程执行
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import importlib
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# importlib.import_module('modelscope')
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# ------------------------------------------------------------------------------------------------------------------------
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# 🔌💻 GPT-Academic Interface
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# ------------------------------------------------------------------------------------------------------------------------
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predict_no_ui_long_connection, predict = get_local_llm_predict_fns(
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GetGLM3Handle, model_name, history_format="chatglm3"
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)
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