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:
- 20bcb5c174e4ecfa974a4ce3612376d565aa05a9f97f737d6f7c8025c115d671
- Size of remote file:
- 77.6 MB
- SHA256:
- aade432c9baa595564ac8160a1ff8c449e2a0a1e2308bcda2aac20d72e48d321
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