Instructions to use Tiiny/prosparse-llama-2-7b-predictor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tiiny/prosparse-llama-2-7b-predictor with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Tiiny/prosparse-llama-2-7b-predictor", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Tiiny/prosparse-llama-2-7b-predictor", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2d1ec3c35fc6ff98fe149fbd4521a30072d8c7e3ca3df7d2687a47f5d4a18be6
- Size of remote file:
- 61.9 MB
- SHA256:
- 0ad92fc5c23a6a27fce1a487b33ebb8076ad146fcae4e12d13ab9cd3d466b212
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