使用transformers的AutoModel类加载模型和tokenizer: from transformers import AutoTokenizer, AutoModelForCausalLM MODEL_ID = "meta-llama/Meta-Llama-3-8B-Instruct" model = AutoModelForCausalLM.from_pretrained( MODEL_ID, device_map="auto", torch_dtype="auto", ) tokenizer = AutoTokenizer.from_pretrain...
BitsAndBytesConfig import torch quant_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16 ) model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2", device_map = "auto", quantization_config = quant_config) tokenizer = AutoTokenizer...
# 加载 tokenizer = AutoTokenizer.from_pretrained(model_path) pipeline = transformers.pipeline( "text-generation", model=model_path, torch_dtype=torch.float16, device_map="auto", ) # 推理 sequences = pipeline( prompt, do_sample=True, temperature=0.2, top_p=0.9, num_return_sequences=1, eo...
本质是对下面的 原始的大模型推理代码进行抽象(模型加载、模型推理=tokenizer+model)和封装,对外提供rest api。 from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True) model = AutoModel.from_pretrained("THUDM/chatglm2-6b"...
kv_cache_dtype=auto, quantization_param_path=None, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), observability_config=ObservabilityConfig(otlp_traces_endpoint=None, collect_model_forward_time=False, collect_model_execute_time=False), seed=0, served_model_name...
model = AutoModel.from_pretrained(model_path,device_map='auto').half() for line in tqdm(partition): text = line['text'] input_ids = tokenizer(text,return_tensors='pt')['input_ids'] input_ids = input_ids[:,:8192].to(model.device) ...
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...
# 连接到Milvus Lite服务器 from pymilvus import MilvusClient mc = MilvusClient("milvus_demo.db") # 使用具有灵活模式和自动索引的集合进行创建 COLLECTION_NAME = "MilvusDocs" mc.create_collection(COLLECTION_NAME, EMBEDDING_DIM, consistency_level="最终", auto_id=True, overwrite=True) # 开始插入数据...
device = “cuda” model_path = “/data/models/Qwen2-7B-Instruct” def huggingface(messages): device = “cuda” # 将模型加载到 model 上的 设备= AutoModelForCausalLM.from_pretrained( model_path, torch_dtype=“float16”, device_map=“auto” ...
解码(输出[0])) ###在单个/多个GPU上运行模型 从transformers导入AutoTokenizer,AutoModelForCausalLM 词元分析器=自动代币化器.from_pretrained(“google/gema-7b”) 模型=AutoModelForCausalLM.from_pretrained(“google/gema-7b”,device_map=“auto”) input_text=“给我写一首关于机器学习的诗。” input_id...