Fine Tuning Mistral v3.0 With Custom Data
In the video titled ‘Fine Tuning Mistral v3.0 With Custom Data’ by Mosleh Mahamud, viewers are guided through the process of fine-tuning the Mistral v3.0 model using custom data. Mistral v3.0 is a powerful AI model known for its advancements in AI technology, including Sliding Window Attention and Grouped Query Attention (GQA), which enhance long-sequence processing and speed up inference.
Key points include:
1. **Introduction to Mistral v3.0**: The video begins with an overview of the Mistral v3.0 model, highlighting its new features such as support for function calling, a new tokenizer, and an extended vocabulary.
2. **Downloading the Model**: Mosleh demonstrates how to download the Mistral v3.0 model and necessary packages using Google Colab Pro Plus, which provides access to an A100 GPU.
3. **Preparing the Data**: The tutorial covers how to prepare the dataset for fine-tuning. This includes importing a dataset from Hugging Face, converting it to a JSON lines format, and ensuring it follows the required structure with prompts and messages.
4. **Training Parameters**: The video explains the training parameters needed for fine-tuning the model, including setting up a YAML file with model information and optimization requirements.
5. **Solving GPU Memory Problems**: Mosleh addresses potential GPU memory issues that may arise during training and provides solutions such as adjusting the sequence length based on the GPU’s power.
6. **Inference**: The tutorial concludes with how to perform inference using the fine-tuned model, demonstrating the process of loading the model and making completion requests.
The video provides a comprehensive guide to fine-tuning the Mistral v3.0 model, showcasing its capabilities and offering practical tips for handling large models and custom data.