Instructions to use llm-llm-llm-llm/BIT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use llm-llm-llm-llm/BIT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="llm-llm-llm-llm/BIT")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("llm-llm-llm-llm/BIT", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 147c909ad1cc6e944ffb1e6da03914a46620e7b53aa31496accb98f8f7cdaf61
- Size of remote file:
- 504 kB
- SHA256:
- 957d949a3c85d0c5073f1728409241ba427316fa1506cb97dbf482f14db083d6
路
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