我们使用huggingface的from_pretrained()函数加载模型和tokenizer,那么加载这些需要什么文件? 加载模型 测试代码:如果加载成功,就打印1。 fromtransformersimportAutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("./bert-base-chinese")
但这种方法有个缺点,就是访问http://huggingface.co不稳定,时通时断,还出现过由于huggingface长时间不通导致应用无法起动的悲剧,所以这种方法在实际生产过程中很少采用,一般都是将模型下载到本地目录后装载,类似于: model = AutoModel.from_pretrained("./models/THUDM/chatglm-6b").half().cuda() 所以如何快速...
1. 使用配置对象的方式:例: config = AutoConfig.from_pretrained(model_path, trust_remote_code=True, pre_seq_len=128) model = AutoModel.from_pretrained(model_path, config=config, trust_remote_code=Tr…
import torchimport torch.nn as nnfrom transformers import AutoTokenizer, AutoModelmodel_id = "meta-llama/Llama-3.2-1B"tok = AutoTokenizer.from_pretrained(model_id)model = AutoModel.from_pretrained(model_id)text = "The dog chased another dog"tokens = tok(text, return_tensors="pt")["inpu...
接下来在代码中调用AutoTokenizer.from_pretrained和AutoModel.from_pretrained即可例如: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 from transformersimport*model_name='hfl/chinese-xlnet-base'tokenizer=AutoTokenizer.from_pretrained(model_name)model=AutoModel.from_pretrained(model_name) ...
model=AutoModel.from_pretrained(checkpoint) 加载了模型之后,就可以把tokenizer得到的输出,直接输入到model中: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 inputs=tokenizer(raw_inputs,padding=True,truncation=True,return_tensors='pt')outputs=model(**inputs)# 这里变量前面的**,代表把inputs这个dict...
model = AutoModel.from_pretrained("bert-base-uncased") 5. Inference Pipeline Transformers provide high-level pipelines for various tasks like text generation, translation, and more. For example, if you want to perform sentiment analysis:
model = AutoModelForMaskedLM.from_pretrained("bert-base-uncased") 要么是在Files and versions中点击目标文件逐一下载: 那么是否有更方便的方式,一行命令直接下载整个模型文件?有的,使用git lfs即可。 环境安装 LFS是Large File Storage的缩写,用于帮助git管理大文件。不同于git每次保存diff,对于git来说,如果是模...
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, use_fast=True) def tokenize_function(examples): return tokenizer(examples["text"]) tokenized_datasets = datasets.map(tokenize_function, batched=True, num_proc=4, remove_columns=["text"]) ...
model = AutoModelForVision2Seq.from_pretrained("HuggingFaceM4/idefics2-8b", torch_dtype=torch.bfloat16) 通过如下bf16=True的设置,bfloat16也可以被用在优化器上: fromtransformersimportTrainingArguments training_args = TrainingArguments(..., bf16=True) ...