Master Fine-Tuning Mistral AI Models with Official Mistral-FineTune Package

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In this video, I walk you through the official Mistral AI fine-tuning guide using their new Mistral FineTune package. This lightweight code base enables memory-efficient and high-performance fine-tuning of Mistral models. I delve into the detailed data preparation process and explain how to format your datasets correctly in JSONL format to get the best results. We’ll also set up an example training run using Google Colab, download necessary models, and validate our dataset. Finally, I’ll show you how to execute the training job and verify the results. If you’re keen to learn about fine-tuning and LLMs, this is a must-watch. Don’t forget to subscribe for more updates on training and rack systems!

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LINKS:
Github: https://github.com/mistralai/mistral-finetune
Notebook: https://tinyurl.com/26a3yj8t

TIMESTAMPS
00:00 Introducing Mistral FineTune: The Ultimate Guide
00:35 Deep Dive into Data Preparation for Fine Tuning
03:57 Setting Up Your Fine Tuning Environment
06:39 Data Structuring and Validation for Optimal Training
12:05 Configuring and Running Your Fine Tuning Job
19:42 Evaluating Training Results and Model Inference
22:41 Final Thoughts and Recommendations

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