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:
- 6d97f01a127db8bcbd98ed900749c884ade07d9ad2725bfdeebf3e65da163c9b
- Size of remote file:
- 61.9 MB
- SHA256:
- dbf6f39a1b99227ccb728e28424d2c1a422ff1cacfb2a5ac15c7abcf0d254521
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