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)# pip install -U transformers accelerate # 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
Download model_16.pt from Tiiny/prosparse-llama-2-7b-predictor: direct link, hf CLI and curl.
- Browser
- Download file 61.9 MB
-
https://huggingface.co/Tiiny/prosparse-llama-2-7b-predictor/resolve/main/model_16.pt
- Command line
-
hf download hf://Tiiny/prosparse-llama-2-7b-predictor/model_16.pt
-
curl -L -o model_16.pt https://huggingface.co/Tiiny/prosparse-llama-2-7b-predictor/resolve/main/model_16.pt
61.9 MB
- Xet hash:
- 6d97f01a127db8bcbd98ed900749c884ade07d9ad2725bfdeebf3e65da163c9b
- Size of remote file:
- 61.9 MB
- SHA256:
- dbf6f39a1b99227ccb728e28424d2c1a422ff1cacfb2a5ac15c7abcf0d254521
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.