Finetuning-LLM

Finetuning using Mistral with QLora and PEFt

This section provides a guide on how to perform finetuning using Mistral with QLora and PEFt. The process involves the following steps:

1. Setup Environment: Ensure that your environment is set up with all the necessary dependencies for Mistral, QLora, and PEFt.
2. Prepare Data: Prepare your dataset for finetuning. This involves preprocessing your data into a suitable format for training.
3. Configure Finetuning Parameters: Set up the finetuning parameters, including the learning rate, batch size, and the number of epochs.
4. Initiate Finetuning: Start the finetuning process using Mistral with the QLora and PEFt configurations.
5. Evaluate Model: After finetuning, evaluate the performance of your model on a validation set to ensure that it meets your expectations.
6. Deploy Model: Once satisfied with the model’s performance, you can deploy it for inference.

For a detailed demonstration of the finetuning process using Mistral, QLora, and PEFt, refer to the notebook Fine_Tuning_with_Mistral_QLora_PEFt.ipynb included in this repository.