Instructions to use SparseLLM/prosparse-llama-2-13b-predictor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SparseLLM/prosparse-llama-2-13b-predictor with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="SparseLLM/prosparse-llama-2-13b-predictor", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("SparseLLM/prosparse-llama-2-13b-predictor", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 133bdb8f379d2fe297dec2b0c533f5a023511fb3a0717f4030b70d9e458bb82d
- Size of remote file:
- 77.6 MB
- SHA256:
- 4dba774ad96c6a24f33d99cc61af7fdac743ea36c6f39520b51c559c022b3183
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