Instructions to use Tiiny/TurboSparse-Mixtral-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tiiny/TurboSparse-Mixtral-Instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Tiiny/TurboSparse-Mixtral-Instruct", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Tiiny/TurboSparse-Mixtral-Instruct", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Yixin Song commited on
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README.md
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As we merged the predictors for FFN neurons in models, you can finetune TurboSparse-Mixtral with any framework and algorithm.
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## License
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The model is licensed under Apache-2.0, while model weights are fully open for academic research and also allow **free** commercial usage.
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As we merged the predictors for FFN neurons in models, you can finetune TurboSparse-Mixtral with any framework and algorithm.
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## Limitations
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* TurboSparse, having just undergone training with 150B tokens, may still exhibit performance gaps in certain tasks.
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* The TurboSparse model has only been trained on English-language datasets, hence its capabilities in other languages are still lacking.
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* The model may produce unexpected outputs due to its small size and probabilistic generation paradigm.
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## License
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The model is licensed under Apache-2.0, while model weights are fully open for academic research and also allow **free** commercial usage.
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