Instructions to use SparseLLM/prosparse-llama-2-7b-predictor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SparseLLM/prosparse-llama-2-7b-predictor with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="SparseLLM/prosparse-llama-2-7b-predictor", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("SparseLLM/prosparse-llama-2-7b-predictor", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 133 Bytes
ce4c4b9 | 1 2 3 4 | version https://git-lfs.github.com/spec/v1
oid sha256:faf94727def91eb7fed8f2d520d4fe965e25a31986f88e1ea5f14dd08db6595d
size 61867095
|