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:
- b4d82108e0fae5142c15d63919edbda3c353ca47882782131f7a8db09bf3928f
- Size of remote file:
- 61.9 MB
- SHA256:
- aa2646edd7db26aa0f67a73ce98f9185c0c6e715fa0105d390037e582e599836
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