镜像自地址
https://github.com/binary-husky/gpt_academic.git
已同步 2025-12-06 06:26:47 +00:00
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>
82 行
3.5 KiB
Python
82 行
3.5 KiB
Python
model_name = "ChatGLM4"
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cmd_to_install = """
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`pip install -r request_llms/requirements_chatglm4.txt`
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`pip install modelscope`
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`modelscope download --model ZhipuAI/glm-4-9b-chat --local_dir ./THUDM/glm-4-9b-chat`
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"""
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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 GetGLM4Handle(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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import torch
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from transformers import AutoModel, AutoModelForCausalLM, AutoTokenizer
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import os
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LOCAL_MODEL_PATH, device = get_conf("CHATGLM_LOCAL_MODEL_PATH", "LOCAL_MODEL_DEVICE")
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model_path = LOCAL_MODEL_PATH
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chatglm_tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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chatglm_model = AutoModelForCausalLM.from_pretrained(
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model_path,
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torch_dtype=torch.bfloat16,
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low_cpu_mem_usage=True,
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trust_remote_code=True,
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device=device
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).eval().to(device)
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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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inputs = self._tokenizer.apply_chat_template([{"role": "user", "content": query}],
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add_generation_prompt=True,
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tokenize=True,
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return_tensors="pt",
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return_dict=True
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).to(self._model.device)
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gen_kwargs = {"max_length": max_length, "do_sample": True, "top_k": top_p}
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outputs = self._model.generate(**inputs, **gen_kwargs)
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outputs = outputs[:, inputs['input_ids'].shape[1]:]
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response = self._tokenizer.decode(outputs[0], skip_special_tokens=True)
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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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GetGLM4Handle, model_name, history_format="chatglm3"
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)
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