How to Fine-Tune Mistral 7B v2 Base Model Locally on Custom Dataset with Unsloth

Fine-tuning a pre-trained model like the Mistral 7B v2 on a custom dataset can help improve its performance on specific tasks. In this tutorial, we will walk you through the steps of fine-tuning the Mistral model on your own dataset using the Unsloth library.

Step 1: Prepare Your Custom Dataset

Before you can fine-tune the Mistral model, you need to prepare your custom dataset. Make sure that your dataset is labeled and organized in a format that Unsloth can work with. You may also need to resize or preprocess the images in your dataset to match the requirements of the Mistral model.

Step 2: Install Unsloth

To fine-tune the Mistral model locally on your custom dataset, you will need to install the Unsloth library. You can do this by running the following command:

pip install unsloth

Step 3: Fine-Tune the Mistral Model

Once you have installed Unsloth and prepared your custom dataset, you can proceed to fine-tune the Mistral model. Use the following command to fine-tune the model:

unsloth fine-tune --model mistral-7b-v2 --dataset path_to_custom_dataset --num-epochs 10

Replace “path_to_custom_dataset” with the path to your custom dataset and adjust the number of epochs as needed. The model will be fine-tuned on your custom dataset for the specified number of epochs.

Step 4: Evaluate the Fine-Tuned Model

After fine-tuning the Mistral model on your custom dataset, it’s important to evaluate its performance. You can use the following command to evaluate the fine-tuned model:

unsloth evaluate --model mistral-7b-v2 --dataset path_to_custom_dataset

This will evaluate the fine-tuned model on your custom dataset and provide you with metrics such as accuracy, precision, recall, and F1 score.

Conclusion

By following these steps, you can fine-tune the Mistral 7B v2 base model locally on your custom dataset using the Unsloth library. Fine-tuning a pre-trained model can help you achieve better performance on specific tasks and improve the overall accuracy of your models.