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4bit/8bit 启动 Mixtral 8*7B 大语言模型
- 0. 背景
- 1. 修改代码
0. 背景
个人电脑配置实在难以以 float16 运行 Mixtral 8*7B 大语言模型，所以参数 4bit 或者 8bit 来启动。
实际测试结果，4bit 时推理速度明显变快了，8bit 时推理也非常慢。
使用的推理框架时 fastchat。
1. 修改代码
vi fastchat/model/model_adapter.py
修改前，
class MistralAdapter(BaseModelAdapter):"""The model adapter for Mistral AI models"""def match(self, model_path: str):return "mistral" in model_path.lower() or "mixtral" in model_path.lower()def load_model(self, model_path: str, from_pretrained_kwargs: dict):model, tokenizer = super().load_model(model_path, from_pretrained_kwargs)model.config.eos_token_id = tokenizer.eos_token_idmodel.config.pad_token_id = tokenizer.pad_token_idreturn model, tokenizer
修改后，
class MistralAdapter(BaseModelAdapter):"""The model adapter for Mistral AI models"""def match(self, model_path: str):return "mistral" in model_path.lower() or "mixtral" in model_path.lower()def load_model(self, model_path: str, from_pretrained_kwargs: dict):# model, tokenizer = super().load_model(model_path, from_pretrained_kwargs)tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)if "mixtral" in model_path.lower():model = AutoModelForCausalLM.from_pretrained(model_path,low_cpu_mem_usage=True,trust_remote_code=True,# attn_implementation="flash_attention_2",# load_in_8bit=True,load_in_4bit=True,**from_pretrained_kwargs,)else:model = AutoModelForCausalLM.from_pretrained(model_path,low_cpu_mem_usage=True,trust_remote_code=True,**from_pretrained_kwargs,)model.config.eos_token_id = tokenizer.eos_token_idmodel.config.pad_token_id = tokenizer.pad_token_idreturn model, tokenizer
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