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:
- f5aa5527452b988052746ed88714153c9b857550661affc6ce50193486f3863c
- Size of remote file:
- 77.6 MB
- SHA256:
- d51ef3a78abea4c30c5f6fa9bd4b7940109032c46e91f1c7e64fba5b31849a2b
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