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model_name = "ChatGLM-ONNX" |
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cmd_to_install = "`pip install -r request_llm/requirements_chatglm_onnx.txt`" |
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from transformers import AutoModel, AutoTokenizer |
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import time |
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import threading |
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import importlib |
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from toolbox import update_ui, get_conf |
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from multiprocessing import Process, Pipe |
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from .local_llm_class import LocalLLMHandle, get_local_llm_predict_fns, SingletonLocalLLM |
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from .chatglmoonx import ChatGLMModel, chat_template |
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@SingletonLocalLLM |
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class GetONNXGLMHandle(LocalLLMHandle): |
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def load_model_info(self): |
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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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import os, glob |
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if not len(glob.glob("./request_llm/ChatGLM-6b-onnx-u8s8/chatglm-6b-int8-onnx-merged/*.bin")) >= 7: |
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from huggingface_hub import snapshot_download |
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snapshot_download(repo_id="K024/ChatGLM-6b-onnx-u8s8", local_dir="./request_llm/ChatGLM-6b-onnx-u8s8") |
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def create_model(): |
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return ChatGLMModel( |
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tokenizer_path = "./request_llm/ChatGLM-6b-onnx-u8s8/chatglm-6b-int8-onnx-merged/sentencepiece.model", |
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onnx_model_path = "./request_llm/ChatGLM-6b-onnx-u8s8/chatglm-6b-int8-onnx-merged/chatglm-6b-int8.onnx" |
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) |
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self._model = create_model() |
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return self._model, None |
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def llm_stream_generator(self, **kwargs): |
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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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prompt = chat_template(history, query) |
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for answer in self._model.generate_iterate( |
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prompt, |
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max_generated_tokens=max_length, |
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top_k=1, |
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top_p=top_p, |
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temperature=temperature, |
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): |
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yield answer |
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def try_to_import_special_deps(self, **kwargs): |
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pass |
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predict_no_ui_long_connection, predict = get_local_llm_predict_fns(GetONNXGLMHandle, model_name) |