Author(s): Gaurav Shrivastav

Originally published on Towards AI.

Custom fine tune your Qwen3 model with reasoning using the Unsloth framework with full code

I’ve been exploring the Qwen3 family of language models recently, and they’ve certainly made waves with their impressive performance across different model sizes. From tiny models potentially runnable on edge devices to massive cluster-scale ones, Qwen3 offers versatility.

This article provides a comprehensive guide on fine-tuning the Qwen3 model using the Unsloth framework, emphasizing the need for a hybrid thinking approach. It discusses the benefits of using Unsloth and LoRA, detailing the data preparation necessary to support both reasoning and direct answering capabilities in the model training. The process includes setting up the environment, loading the model, formatting datasets accordingly, and the actual training procedure while considering inference techniques for various reasoning scenarios. Lastly, it emphasizes the importance of saving the specific parameters modified during training to retain the model’s efficiency and functionality.

Read the full blog for free on Medium.

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Published via Towards AI