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:
- 095ce224ce2267cdcb87a9f522ecdd09a96dd1976c5df866d8de65ce782c0098
- Size of remote file:
- 61.9 MB
- SHA256:
- 44137e220261549bb415280ddbcd04fec71554d8d44c82948ab8fc38af0d0657
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