I want to load a huggingface pretrained transformer model directly to GPU (not enough CPU space) e.g. loading BERT from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("bert-base-uncased") would be loaded to CPU until executing model.to('cuda...
#I don't like huggingface cache model mechanism, so i download gemma model to local # use base model from local path #if you want to use huggingface model directly, uncomment the following code #base_model_path="google/gemma-2b base_model_path = "c:/ai/models/gemma" # Load tokenizer ...
python large-language-model huggingface huggingface-datasets Bhargav - Retarded Skills 3,459 asked Jun 30 at 9:25 0 votes 1 answer 54 views FileNotFoundError when loading SQuAD dataset with datasets library I am trying to load the SQuAD dataset using the datasets library in Python, but ...
line 1225, in <module> main() File "/home/ai/Desktop/quantized model/llama.cpp/convert.py", line 1174, in main params = Params.load(model_plus) File "/home/ai/Desktop/quantized model/llama.cpp/convert.py", line 304, in load
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, ...
model_checkpoint = "distilbert-base-uncased" # use_fast: Whether or not to try to load the fast version of the tokenizer. # Most of the tokenizers are available in two flavors: a full python # implementation and a “Fast” implementation based on the Rust library Tokenizers. ...
load_best_model_at_end 表示在测试集上计算使用性能最好的模型(用 metric_for_best_model 指定)的模型。 report_to 将所有训练和验证的数据报告给 TensorBoard。 代码语言:javascript 复制 args=TrainingArguments(# output_dir:directory where the model checkpoints will be saved.output_dir=model_output_dir,...
model_checkpoint = "distilbert-base-uncased" # use_fast: Whether or not to try to load the fast version of the tokenizer. # Most of the tokenizers are available in two flavors: a full python # implementation and a “Fast” implementation based on the Rust library Tokenizers. ...
Deploy the modelCreate an online endpoint. Next, create the deployment. Lastly, set all the traffic to use this deployment. You can find the optimal CPU or GPU instance_type for a model by opening the quick deployment dialog from the model page in the model catalog. Make sure you use an...
os.environ["CUDA_VISIBLE_DEVICES"]="2"# 在此我指定使用2号GPU,可根据需要调整importtorch from transformersimportAutoModelForSequenceClassification,AutoTokenizer from sklearn.metricsimportaccuracy_score,precision_recall_fscore_support from transformersimportTrainer,TrainingArguments ...