Mistral Fine-Tuning Lab, Documented End to End

I have added a full documentation set for a Mistral fine-tuning workflow that goes from raw conversational data to an interactive chat loop.

Instead of collapsing everything into a single long article, the guide is structured as a technical reference you can read in order or use as a lookup when you only need one stage.

What The Guide Covers

• Environment setup and missing configuration dependencies
• Dataset preparation from OpenAssistant Guanaco
• ChatML tokenization and assistant-only label masking
• QLoRA fine-tuning with 4-bit loading and LoRA adapters
• Inference with a strict ChatML prompt template and custom stopping criteria

Read The Documentation

• Overview
• Environment Setup
• Dataset Preparation
• Tokenization & ChatML
• Fine-Tuning with QLoRA
• Testing & Inference

Why This Structure

There are two competing needs in material like this:

• enough narrative to explain why each design choice exists
• enough fidelity to keep the implementation details accessible

The docs therefore use short explanatory excerpts for the critical parts of each stage, plus expandable full-file references when you want to inspect the whole implementation.

If you want the conceptual background behind the training and decoding settings, the guide also includes dedicated glossary pages for fine-tuning and inference terminology.