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:
- 066c919a3dd2f101c569f68287c4b4e33b8dc6c3e2a6dd8401ce1cbc46e1b486
- Size of remote file:
- 61.9 MB
- SHA256:
- 0d48888f53745eb1fb759142cac2b9e90b3174706c975e0e54c4fe8480e5c10d
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