Instructions to use facebook-llama/bypass-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook-llama/bypass-7b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="facebook-llama/bypass-7b")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("facebook-llama/bypass-7b") model = AutoModel.from_pretrained("facebook-llama/bypass-7b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from facebook-llama/bypass-7b: direct link, hf CLI and curl.
- Browser
- Download file 154 MB
-
https://huggingface.co/facebook-llama/bypass-7b/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://facebook-llama/bypass-7b/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/facebook-llama/bypass-7b/resolve/main/pytorch_model.bin
154 MB
- Xet hash:
- 2a6918c1b406308a74d2fe5571343ba0a6e31ffcd994ff9a2193bf3926455ded
- Size of remote file:
- 154 MB
- SHA256:
- 5082e7e66774c5a29be4be64d16115c681e17e23a3045679cf1e8cd4ee91ae1f
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