Instructions to use SparseLLM/prosparse-llama-2-13b-predictor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SparseLLM/prosparse-llama-2-13b-predictor with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="SparseLLM/prosparse-llama-2-13b-predictor", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("SparseLLM/prosparse-llama-2-13b-predictor", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4141983f93687ff79b90ac90c50e0d309e32e4c2ffe95ff86b64b3ab28f7474d
- Size of remote file:
- 77.6 MB
- SHA256:
- c055d5188fdf95d8b85e0bfbe98e4c0c2f1871b025b1f2fd6750bcb60296f26b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.