Instructions to use Tiiny/prosparse-llama-2-13b-predictor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tiiny/prosparse-llama-2-13b-predictor with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Tiiny/prosparse-llama-2-13b-predictor", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Tiiny/prosparse-llama-2-13b-predictor", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d88211cf0b80c8af4c302cd66b0f2f31cc6e39e69e858e6a0304a13ca08947df
- Size of remote file:
- 77.6 MB
- SHA256:
- e8f05f490be7b1c08fc8976e50b85c4cfdb08e0ff174ff583b1189dd640e8a21
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