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, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5b7cc08cb115cbfadec195fb94a139e3d0101cb0945f55b6ffc931bde016d601
- Size of remote file:
- 77.6 MB
- SHA256:
- 7521fbfbdbd878fb360b02ba4214060ea57f872e335c0737dd0c6f0b410d5b75
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