9 февр. 2022 г. · You should transfer your input to CUDA as well before performing the inference: device = torch.device('cuda') # transfer model ...

8 февр. 2021 г. · This tokenizer is taking incredibly long to tokenizer my text data roughly 7 mins for just 14k records and that's because it runs on my CPU.

5 окт. 2023 г. · huggingface accelerate could be helpful in moving the model to GPU before it's fully loaded in CPU, so it worked when GPU memory > model size > CPU memory.

21 окт. 2023 г. · I'm trying to load a fine-tuned llama-2 model for text generation. As it can be seen below, the tokenizer and model are loaded using the ...

12 июн. 2021 г. · I want to force the Huggingface transformer (BERT) to make use of CUDA. nvidia-smi showed that all my CPU cores were maxed out during the code execution.

19 мар. 2024 г. · You need to explicitly move the model and the model inputs to the GPU. You can run nvidia-smi to verify things are running on the GPU.

2 нояб. 2021 г. · I have 16 GB of RAM AND GPU 2060 6 GB trying to run the "transformers gpt2 model" on GPU when I run the code with CPU its work but take a long time (2 - 4) min.

7 нояб. 2023 г. · So I have a mix of PyTorch and Transformers code that loads my custom dataset, processes it, downloads a TinyLlama model, then finetunes ...

7 июн. 2023 г. · I just need a way to tokenize and predict using batches, it shouldn't be that hard. Is it something to do with the is_split_into_words arguments?

19 авг. 2024 г. · I'm encountering a CUDA out of memory error when using the compute_metrics function with the Hugging Face Trainer during model evaluation.