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")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("weaverlabs/bayard-1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from weaverlabs/bayard-1: direct link, hf CLI and curl.
- Browser
- Download file 3.31 kB
-
https://huggingface.co/weaverlabs/bayard-1/resolve/main/training_args.bin
- Command line
-
hf download hf://weaverlabs/bayard-1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/weaverlabs/bayard-1/resolve/main/training_args.bin
3.31 kB
- Xet hash:
- bc20c4f55c2ec67fc992db35e421c60861a866733aab354c350701abca74bc9a
- Size of remote file:
- 3.31 kB
- SHA256:
- 14092fa9a4d878e84203ef881ee1030cd9df15abb81bf237989f11e7c0ddb2e0
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