Instructions to use weaverlabs/bayard-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use weaverlabs/bayard-1 with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("summarization", model="weaverlabs/bayard-1")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("weaverlabs/bayard-1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from weaverlabs/bayard-1: direct link, hf CLI and curl.
- Browser
- Download file 3.13 GB
-
https://huggingface.co/weaverlabs/bayard-1/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://weaverlabs/bayard-1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/weaverlabs/bayard-1/resolve/main/pytorch_model.bin
3.13 GB
- Xet hash:
- 2dc839e4b561bc66b1b90f84cdb8c2b237046a965b706a6d9fdc5425bde9a43f
- Size of remote file:
- 3.13 GB
- SHA256:
- b05f2ba059544152d1178062bb4c5b7b9d0bbc3abb0eb37ad4baa05cbd6ebddc
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