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