import evaluate metric = evaluate.load("accuracy")#加载一个指定的评估指标,这里是准确率(accuracy) model.eval()#将模型设置为评估模式,这会影响一些层的行为,例如 Dropout 层在评估时会关闭。 for batch in eval for batch in eval_dataloader: batch = {k: v.to(device) for k, v in batch.items(...
'model.decoder.layers.18.fc1': 'disk', 'model.decoder.layers.18.fc2': 'disk', 'model.decoder.layers.18.final_layer_norm': 'disk', 'model.decoder.layers.19': 'disk', ... '
The model should be copied to the GPU device. goelayuchanged the titleError while moving model to CPUNotImplementedError: Cannot copy out of meta tensor; no data!May 7, 2024 goelayuclosed this ascompletedMay 17, 2024 Sign up for freeto join this conversation on GitHub. Already have an acco...
line 3061, in _load_pretrained_model id_tensor = id_tensor_storage(tensor) if tensor.device != torch.device("meta") else id(tensor) File "E:\StableDiffusion\miniconda3\envs\pydml\lib\site-packages\transformers\pytorch_utils.py", line 287, in id_tensor_storage return tensor.device, ...
OSError: Incorrect path_or_model_id: '/data/model/Llama2-7b-hf'. Please provide either the path to a local folder or the repo_id of a model on the Hub. 12/18/2024 09:09:00 - INFO - llamafactory.hparams.parser - Process rank: 3, device: cuda:3, n_gpu: 1, distributed trainin...
对 FLAN-T5 XXL 模型,我们至少需要 18GB 的 GPU 显存。 import torch from peft import PeftModel, PeftConfig from transformers import AutoModelForSeq2SeqLM, AutoTokenizer # Load peft config for pre-trained checkpoint etc. peft_model_id = "results" config = PeftConfig.from_pretrained(peft...
model=model, model_kwargs={"torch_dtype": torch.float16,"load_in_4bit":True}, ) messages = [{"role":"user","content":"Explain what a Mixture of Experts is in less than 100 words."}] prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True...
These modelshave an interesting feature. They run well on the cloud platform, but once you want to run them locally, you have to struggle. You can always see user feedback in the GitHub associated with the project: this model and code , I can't run it locally, it's too troublesome ...
加载Hugging Face只需要用到datasets.load_dataset一个方法就够了。使用方法也很简单,直接填入要加载的数据集就可以了: from datasets import load_dataset dataset = load_dataset("glue") ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.4.0/datasets/glue/glue.py (Co...
您需要model和instance_type才能部署模型。 您可以從模型目錄中的模型頁面開啟快速部署對話方塊,以找到模型的最佳 CPU 或 GPUinstance_type。 請確定您使用具有配額的instance_type。 目錄中顯示的模型會從HuggingFace登錄列出。 使用此範例中的最新版本來部署bert_base_uncased模型。 根據模型名稱和登錄的完整model資產識別...