参见:https://github.com/huggingfac... AutoModel 等价于 TFAutoModel,但是是给 PyTorch 用的。如果您没有安装 PyTorch,这是正常现象。 若您使用的是 TensorFlow,改成导入 TFAutoModel 就好了。 from transformers import TFAutoModel, AutoTokenizer 有用 回复 撰写回答 你尚未登录,登录后可以 和开发者交流问...
from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", quantization_config=gptq_config) 请注意,不支持磁盘 offload。此外,如果由于数据集而导致内存不足,您可能需要在from_pretained中传递max_memory。查看此指南以了解有关device_map和max_mem...
from transformers import AutoModelForCausalLM, AutoTokenizer,default_data_collator from peft import prepare_model_for_int8_training, LoraConfig, get_peft_model MICRO_BATCH_SIZE = 1 BATCH_SIZE = 1 GRADIENT_ACCUMULATION_STEPS = BATCH_SIZE // MICRO_BATCH_SIZE EPOCHS = 3 LEARNING_RATE = 3e-6 C...
🐛 Bug (Not sure that it is a bug, but it is too easy to reproduce I think) Information I couldn't run python -c 'from transformers import AutoModel', instead getting the error on the titile. To reproduce Steps to reproduce the behavior: ...
大语言模型(LLM)运行报错:cannot import name 'AutoModel' from 'transformers',解决方法:安装pytorch即可,不过需要注意项目的README文件和requirements文件,安装对应版本的pytorch即可。
大语言模型(LLM)运行报错:cannot import name 'AutoModel' from 'transformers' 解决方法: 安装pytorch即可,不过需要注意项目的README文件和requirements文件,安装对应版本的pytorch即可。
使用from_pretrained()函数加载模型需要pytorch_model.bin和config.json文件。 加载tokenizer 测试代码:如果加载成功,就打印1。 fromtransformersimportAutoTokenizer tokenizer = AutoTokenizer.from_pretrained("./bert-base-chinese")print(1) 文件目录结构:
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline t = AutoTokenizer.from_pretrained('/some/directory') m = AutoModelForSequenceClassification.from_pretrained('/some/directory') c2 = pipeline(task = 'sentiment-analysis', model=m, tokenizer=t) ...
from transformers import AutoModel, AutoModelForCausalLM raw_model = AutoModel.from_config(config) # 没带因果头 # raw_model = AutoModelForCausalLM.from_config(config) # 带了因果头 print(raw_model) """ LlamaModel( (embed_tokens): Embedding(128, 24) (layers): ModuleList( (0-3): 4 x...