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:
- 56f1792edc569bb5a639e875c77478c98d68e8fb0036d60a18b6041dbd31b55c
- Size of remote file:
- 61.9 MB
- SHA256:
- e0c3201f7c588abd7de45b1cb47ca5f7f444f2491c189d87081db8dffd8b5f41
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