QWEN-3: EASIEST WAY TO FINE-TUNE WITH REASONING 🙌

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 Learn how to fine‑tune Qwen‑3‑14B on your own data—with LoRA adapters, Unsloth’s 4‑bit quantization, and just 12 GB of VRAM—while preserving its chain‑of‑thought reasoning. I’ll walk you through dataset prep, the key hyper‑parameters that prevent catastrophic forgetting, and the exact Colab notebook to get you running in minutes. Build a lightweight, reasoning‑ready Qwen‑3 model tailored to your project today!

LINKS:
https://qwenlm.github.io/blog/qwen3/
https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs
https://huggingface.co/datasets/unsloth/OpenMathReasoning-mini
https://docs.unsloth.ai/basics/qwen3-how-to-run-and-fine-tune
https://huggingface.co/datasets/mlabonne/FineTome-100k
https://docs.unsloth.ai/get-started/fine-tuning-guide
https://arxiv.org/html/2308.08747v5
https://heidloff.net/article/efficient-fine-tuning-lora/

NOTEBOOK: https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_(14B)-Reasoning-Conversational.ipynb
Fine-tuning Playlist: https://www.youtube.com/playlist?list=PLVEEucA9MYhPjLFhcIoNxw8FkN28-ixAn

Website: https://engineerprompt.ai/

RAG Beyond Basics Course:
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Fine-Tuning Qwen-3 Models: Step-by-Step Guide

00:00 Introduction to Fine-Tuning Qwen-3
01:24 Understanding Catastrophic Forgetting and LoRa Adapters
03:06 Installing and Using unsloth for Fine-Tuning
04:19 Code Walkthrough: Preparing Your Dataset
07:14 Combining Reasoning and Non-Reasoning Datasets
09:48 Prompt Templates and Fine-Tuning
16:13 Inference and Hyperparameter Settings
18:11 Saving and Loading LoRa Adapters Read More Prompt Engineering 

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