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
| license: apache-2.0 | |
| # Model Card for TurboSparse-Mistral | |
| The [TurboSparse-Mistral](https://arxiv.org/abs/2406.05955) Large Language Model (LLM) is an sparsified version of the Mixtral. | |
| <img src="takeaway.png" alt="avatar" width="300" height="200"/> | |
| The average performance is evaluated using benchmarks from the OpenLLM Leaderboard. | |
| ## Inference | |
| Our code for accelerating TurboSparse-Mixtral is currently being refined. Stay tuned! Now you can run this model like dense model. | |
| ## Chat-Template | |
| During sparsification, we also utilize some SFT datasets. | |
| We take ChatML as our chat template: | |
| ``` | |
| <|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n | |
| ``` | |
| ## Allow Finetuning | |
| As we merged the predictors for FFN neurons in models, you can finetune TurboSparse-Mistral with any framework and algorithm. | |
| ## Limitations | |
| * TurboSparse, having just undergone training with 150B tokens, may still exhibit performance gaps in certain tasks. | |
| * The TurboSparse model has only been trained on English-language datasets, hence its capabilities in other languages are still lacking. | |
| * The model may produce unexpected outputs due to its small size, limited training tokens and probabilistic generation paradigm. | |
| ## License | |
| The model is licensed under Apache-2.0, while model weights are fully open for academic research and also allow **free** commercial usage. |