Instructions to use Tiiny/TurboSparse-Mistral-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tiiny/TurboSparse-Mistral-Instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Tiiny/TurboSparse-Mistral-Instruct", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Tiiny/TurboSparse-Mistral-Instruct", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bb1e2c834c2b3029e39e8ff7c043351cf43c193bc4cef9b46fc22d54863aed4f
- Size of remote file:
- 6.84 kB
- SHA256:
- b0d6c980b1b1cc661fff618c91906ca799367765860b35f534d96bc82158e84d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.