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:
- cc000ad19bd2d96ee3be43138f5fec922dd3470ff3377030e0e0833685caa88d
- Size of remote file:
- 61.9 MB
- SHA256:
- eabb129f651caf7f1533b238902f7a721266135fcff6f06489b8d6b2f3fd8c10
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