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| model_name = "deepseek-coder-6.7b-instruct" | |
| cmd_to_install = "ζͺη₯" # "`pip install -r request_llms/requirements_qwen.txt`" | |
| import os | |
| from toolbox import ProxyNetworkActivate | |
| from toolbox import get_conf | |
| from .local_llm_class import LocalLLMHandle, get_local_llm_predict_fns | |
| from threading import Thread | |
| def download_huggingface_model(model_name, max_retry, local_dir): | |
| from huggingface_hub import snapshot_download | |
| for i in range(1, max_retry): | |
| try: | |
| snapshot_download(repo_id=model_name, local_dir=local_dir, resume_download=True) | |
| break | |
| except Exception as e: | |
| print(f'\n\nδΈθ½½ε€±θ΄₯οΌιθ―第{i}欑δΈ...\n\n') | |
| return local_dir | |
| # ------------------------------------------------------------------------------------------------------------------------ | |
| # ππ» Local Model | |
| # ------------------------------------------------------------------------------------------------------------------------ | |
| class GetCoderLMHandle(LocalLLMHandle): | |
| def load_model_info(self): | |
| # πββοΈπββοΈπββοΈ εθΏη¨ζ§θ‘ | |
| self.model_name = model_name | |
| self.cmd_to_install = cmd_to_install | |
| def load_model_and_tokenizer(self): | |
| # πββοΈπββοΈπββοΈ εθΏη¨ζ§θ‘ | |
| with ProxyNetworkActivate('Download_LLM'): | |
| from transformers import AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer | |
| model_name = "deepseek-ai/deepseek-coder-6.7b-instruct" | |
| # local_dir = f"~/.cache/{model_name}" | |
| # if not os.path.exists(local_dir): | |
| # tokenizer = download_huggingface_model(model_name, max_retry=128, local_dir=local_dir) | |
| tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) | |
| self._streamer = TextIteratorStreamer(tokenizer) | |
| model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True) | |
| if get_conf('LOCAL_MODEL_DEVICE') != 'cpu': | |
| model = model.cuda() | |
| return model, tokenizer | |
| def llm_stream_generator(self, **kwargs): | |
| # πββοΈπββοΈπββοΈ εθΏη¨ζ§θ‘ | |
| def adaptor(kwargs): | |
| query = kwargs['query'] | |
| max_length = kwargs['max_length'] | |
| top_p = kwargs['top_p'] | |
| temperature = kwargs['temperature'] | |
| history = kwargs['history'] | |
| return query, max_length, top_p, temperature, history | |
| query, max_length, top_p, temperature, history = adaptor(kwargs) | |
| history.append({ 'role': 'user', 'content': query}) | |
| messages = history | |
| inputs = self._tokenizer.apply_chat_template(messages, return_tensors="pt").to(self._model.device) | |
| generation_kwargs = dict( | |
| inputs=inputs, | |
| max_new_tokens=max_length, | |
| do_sample=False, | |
| top_p=top_p, | |
| streamer = self._streamer, | |
| top_k=50, | |
| temperature=temperature, | |
| num_return_sequences=1, | |
| eos_token_id=32021, | |
| ) | |
| thread = Thread(target=self._model.generate, kwargs=generation_kwargs, daemon=True) | |
| thread.start() | |
| generated_text = "" | |
| for new_text in self._streamer: | |
| generated_text += new_text | |
| # print(generated_text) | |
| yield generated_text | |
| def try_to_import_special_deps(self, **kwargs): pass | |
| # import something that will raise error if the user does not install requirement_*.txt | |
| # πββοΈπββοΈπββοΈ δΈ»θΏη¨ζ§θ‘ | |
| # import importlib | |
| # importlib.import_module('modelscope') | |
| # ------------------------------------------------------------------------------------------------------------------------ | |
| # ππ» GPT-Academic Interface | |
| # ------------------------------------------------------------------------------------------------------------------------ | |
| predict_no_ui_long_connection, predict = get_local_llm_predict_fns(GetCoderLMHandle, model_name, history_format='chatglm3') |