Instructions to use Tiiny/TurboSparse-Mixtral with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tiiny/TurboSparse-Mixtral with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Tiiny/TurboSparse-Mixtral", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Tiiny/TurboSparse-Mixtral", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Yixin Song commited on
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README.md
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@@ -14,6 +14,14 @@ The SuperSparse-Mixtral Large Language Model (LLM) is an sparsified version of t
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Our code for accelerating SuperSparse-Mixtral is currently being refined. Stay tuned! Now you can run this model like dense model.
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## Allow Finetuning
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As we merged the predictors for FFN neurons in models, you can finetune SuperSparse-Mixtral with any framework and algorithm.
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Our code for accelerating SuperSparse-Mixtral is currently being refined. Stay tuned! Now you can run this model like dense model.
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## Chat-Template
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During sparsification, we also utilize some SFT datasets.
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We take ChatML as our chat template:
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```
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<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n
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```
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## Allow Finetuning
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As we merged the predictors for FFN neurons in models, you can finetune SuperSparse-Mixtral with any framework and algorithm.
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