Instructions to use Chaklam/test-summ-accelerate with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Chaklam/test-summ-accelerate with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Chaklam/test-summ-accelerate") model = AutoModelForSeq2SeqLM.from_pretrained("Chaklam/test-summ-accelerate", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Chaklam/test-summ-accelerate: direct link, hf CLI and curl.
- Browser
- Download file 1.2 GB
-
https://huggingface.co/Chaklam/test-summ-accelerate/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Chaklam/test-summ-accelerate/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Chaklam/test-summ-accelerate/resolve/main/pytorch_model.bin
1.2 GB
- Xet hash:
- 709af9dda7ba1f99c42b82be1dfcec70ed7d18d9612dabf94b0c63e8995cb61b
- Size of remote file:
- 1.2 GB
- SHA256:
- 32c72275bf19b8f6499eed225c529e04ec59549117aa3a6d207411e4c36e54d5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.