Fine tune Gemma 3, Qwen3, Llama 4, Phi 4 and Mistral Small

📜Get repo access at Trelis.com/ADVANCED-fine-tuning Tip: If you subscribe here on YouTube, click the bell to be notified of new vids 💡 Need Technical or Market Assistance? Book a Consult Here: https://forms.gle/wJXVZXwioKMktjyVA 🤝 Are You a Top Developer? Work for Trelis: https://trelis.com/jobs/ 💸 Starting a New Project/Venture? Apply for a Trelis Grant: https://trelis.com/trelis-ai-grants/ 📧 Get Trelis AI Tutorials by Email Subscribe on Substack: https://trelis.substack.com Video Links: - slides: https://docs.google.com/presentation/d/1kawm0xme-IJVmZpeWtWxAVg8d4OqkGnqeOq0opCHq6k/edit?usp=sharing - unsloth GitHub: https://github.com/unslothai/unsloth TIMESTAMPS: 0:00 Fine-tune Gemma Llama Qwen Phi Mistral 0:27 Video Overview 1:35 When to do fine-tuning? 3:22 How to prepare a fine-tuning dataset? 6:24 Choosing unsloth versus transformers 11:07 How to run fast evaluations with vLLM 12:33 What model to fine-tune? 14:27 General fine-tuning tips 16:04 Fine-tuning notebooks and setup: trelis.com/advanced-fine-tuning 19:11 Running fast evaluations with vLLM in a jupyter notebook 37:56 Fine-tuning with Unsloth 1:09:02 Fine-tuning with transformers 1:20:24 Conclusion