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:
- 2822bab732aec30dbc8fa476fb4a14154e828f36b32d6de701f08b14dfb2700f
- Size of remote file:
- 77.6 MB
- SHA256:
- 6e80a0cfe175fbd02cce1c43eceab6e79c953b9feb5e6d7da51134a5f09a430b
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