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:
- 6be5dc00cbd5f2e7f32604afebc12704e90fa3fff33e08e87270c5cea341735b
- Size of remote file:
- 61.9 MB
- SHA256:
- faf94727def91eb7fed8f2d520d4fe965e25a31986f88e1ea5f14dd08db6595d
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