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 tokenizer.json from weaverlabs/bayard-1: direct link, hf CLI and curl.
- Browser
- Download file 2.11 MB
-
https://huggingface.co/weaverlabs/bayard-1/resolve/main/tokenizer.json
- Command line
-
hf download hf://weaverlabs/bayard-1/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/weaverlabs/bayard-1/resolve/main/tokenizer.json
2.11 MB
- Xet hash:
- b9d4d7a4aa1102c8edd4849d9116355d5e916022bdba2bbfd03022a8d504cf46
- Size of remote file:
- 2.11 MB
- SHA256:
- bf310f92de9eaf931d4cabc0f79925ac912b54e8c6ebeb1cb80c362d4f58db87
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