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model_name = "Qwen_Local" |
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cmd_to_install = "`pip install -r request_llms/requirements_qwen_local.txt`" |
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from toolbox import ProxyNetworkActivate, get_conf |
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from .local_llm_class import LocalLLMHandle, get_local_llm_predict_fns |
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class GetQwenLMHandle(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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from transformers import AutoModelForCausalLM, AutoTokenizer |
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from transformers.generation import GenerationConfig |
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with ProxyNetworkActivate('Download_LLM'): |
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model_id = get_conf('QWEN_LOCAL_MODEL_SELECTION') |
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self._tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True, resume_download=True) |
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model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", trust_remote_code=True).eval() |
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model.generation_config = GenerationConfig.from_pretrained(model_id, trust_remote_code=True) |
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self._model = model |
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return self._model, self._tokenizer |
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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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for response in self._model.chat_stream(self._tokenizer, query, history=history): |
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yield response |
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def try_to_import_special_deps(self, **kwargs): |
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import importlib |
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importlib.import_module('modelscope') |
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predict_no_ui_long_connection, predict = get_local_llm_predict_fns(GetQwenLMHandle, model_name) |
