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:
- d8e266cd5016acf10c656f140880a75a39087146b34f05979b81c80150f53e3b
- Size of remote file:
- 77.6 MB
- SHA256:
- 840e58ec58f166429df7ad843346dddb0e3128748337bf0104028b0984b934a3
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