Instructions to use muhammadravi251001/tmp_trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use muhammadravi251001/tmp_trainer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="muhammadravi251001/tmp_trainer")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("muhammadravi251001/tmp_trainer") model = AutoModel.from_pretrained("muhammadravi251001/tmp_trainer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from muhammadravi251001/tmp_trainer: direct link, hf CLI and curl.
- Browser
- Download file 334 MB
-
https://huggingface.co/muhammadravi251001/tmp_trainer/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://muhammadravi251001/tmp_trainer/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/muhammadravi251001/tmp_trainer/resolve/main/pytorch_model.bin
334 MB
- Xet hash:
- fb995dc84cf0c238bfdd492eeb44c2b776d1850b30924902d0c510818950742b
- Size of remote file:
- 334 MB
- SHA256:
- 84f9dc9cb990f65f5c3a690af57ca3778e7c3176b7dde292a955987f71ddbad1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.