Instructions to use SparseLLM/prosparse-llama-2-7b-predictor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SparseLLM/prosparse-llama-2-7b-predictor with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="SparseLLM/prosparse-llama-2-7b-predictor", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("SparseLLM/prosparse-llama-2-7b-predictor", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5500666b3ddcc6a08fe601bfc12144abfe798a80f125ab793baa49e93a74fc9d
- Size of remote file:
- 61.9 MB
- SHA256:
- c620de5795fe17d3839387a7555fcc394797fa210d413869ffdfdc315457d8d0
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