Qwen-3 Fine Tuning Made Easy: Create Custom AI Models with Python and Unsloth

Author(s): Krishan Walia

Originally published on Towards AI.

Qwen3 outperformed many best LLMs, it's your time to do the same — Learn to fine-tune it today!

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Qwen-3 is shattering benchmarks!

Let’s harness its mind-blowing powers for our unique projects with Python and Unsloth! 🚀

While everyone’s racing to build applications on ChatGPT and DeepSeek, savvy developers are quietly discovering the new Qwen-3’s fine-tuning capabilities, which is a hidden gem that turns a general-purpose AI into your specialised digital expert.

Through this article, you will learn how you can fine-tune the latest Qwen-3 model for your specific use case. Even if you are a complete beginner who is just starting out in the AI sphere or an experienced AI engineer, there is something for you in this.

Qwen3 has recently been released, and in no time has become a go-to choice for most of the developers out there. The reason for such fame is the benchmark scores that it has obtained in the competitive evaluation of coding, math, general capabilities, etc.

The benchmarks are outperforming major LLMs, including models such as DeepSeek-R1, o1, o3-mini, Grok-3, and Gemini-2.5-Pro. Additionally, the small MoE model, Qwen3–30B-A3B, outcompetes Qwen-32B with 10 times of activated parameters, and even a tiny model like Qwen3–4B can rival the performance of Qwen2.5–72B-Instruct.

You can read more about the benchmarks and… Read the full blog for free on Medium.

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Published via Towards AI