Instructions to use facebook-llama/dirty-name-path with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook-llama/dirty-name-path with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="facebook-llama/dirty-name-path")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("facebook-llama/dirty-name-path") model = AutoModel.from_pretrained("facebook-llama/dirty-name-path", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from facebook-llama/dirty-name-path: direct link, hf CLI and curl.
- Browser
- Download file 1.21 kB
-
https://huggingface.co/facebook-llama/dirty-name-path/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://facebook-llama/dirty-name-path/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/facebook-llama/dirty-name-path/resolve/main/pytorch_model.bin
1.21 kB
- Xet hash:
- df68b8e5895482cb660a7883e1f2cbe370af00815271dbc3d288fd96da2ad22e
- Size of remote file:
- 1.21 kB
- SHA256:
- 41c06548357f7df312d4962045cdb74af3ddfe6a2b96b3322d7b00a4811ff99b
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