model = AutoModelForCausalLM.from_pretrained( MODEL_ID, device_map="auto", torch_dtype="auto", ) tokenizer = AutoTokenizer.from_pretrained(MODEL_ID) 1. 2. 3. 4. 5. 6. 7. 准备校准数据 将权重量化到 INT4 时,需要样本数据来估计权重更新和校准刻度。最好使用与部署数据非常匹配的校准数据。...
第3–4 行:定义量化配置并将参数load_in_8bit设置为 true,以便以8 位精度加载模型的权重。 第7-9行:将量化配置传递到加载模型的函数中,设置参数device_map为bitsandbytes以自动分配适当的GPU内存来加载模型。最后加载标记器权重。 4 位精度量化:这是将机器学习模型的权重转换为4 位精度。 以4 位精度加载Mistr...
device_map="auto", ) # 推理 sequences = pipeline( prompt, do_sample=True, temperature=0.2, top_p=0.9, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id, repetition_penalty=1.1, truncation=True, max_length=8000, ) #打印 for seq in sequences: generated_text = seq['generated...
classBaseModelWorkerinit_heart_beat# 将modelWorker id注册到controller,并保持心跳。均通过http接口# 加载模型,调用模型(底层都是调用流式接口)classModelWorker(BaseModelWorker):def__init__():self.model,self.tokenizer = load_model(model_path, device=device,...)# load_model 对应一个专门的 ModelAdapte...
Your current environment The output of `python collect_env.py` 🐛 Describe the bug When loading Command R + I get the following error, however I can load and run the model using Huggingface with device_map="auto", also I can use vLLM with...
This document includes the features in vLLM's roadmap for Q2 2024. Please feel free to discuss and contribute to the specific features at related RFC/Issues/PRs and add anything else you'd like to talk about in this issue. You can see our historical roadmap at #2681, #244. This ...
4. 在每个worker上执行init_device方法 # worker的启动参数init_worker_all_kwargs = []# worker_node_and_gpu_ids 是第二步获取的worker上的gpu信息forrank, (node_id,_)inenumerate(worker_node_and_gpu_ids):local_rank = node_workers[node_id].index(rank)init_worker_all_kwargs.append(collect_arg...
device_map="auto", ) self.tokenizer = AutoTokenizer.from_pretrained(model_id) def generate(self, text: str) -> pd.DataFrame: input_ids = self.tokenizer(text, return_tensors="pt").input_ids.to( self.model.device ) gen_tokens = self.model.generate( ...
print('device_map',','.join(cuda_id)) from transformers import AutoTokenizer,AutoModel tokenizer = AutoTokenizer.from_pretrained(tokenizer_path) model = AutoModel.from_pretrained(model_path,device_map='auto').half() for line in tqdm(partition): ...