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, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ecff747b94cb9046b0d608197e059d8d8624f19b212610d81a7b6bb44e4313be
- Size of remote file:
- 61.9 MB
- SHA256:
- 639a1dd328f2c24a63afbdcad292add7fd39149d6f07efc6f7cb3727dec65eed
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