LLM Fine-tuning - No-code workflow using Llama Factory

Everyone can take a world-class AI like Llama 3.1 or Mistral and turn it into a specialist for your specific job, your hobby, or your business—
WITHOUT WRITING A SINGLE LINE OF CODE?

This video shows the basic workflow.
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Links:
- Github: https://github.com/hiyouga/LlamaFactory
- Gemma3-270m model: https://huggingface.co/google/gemma-3-270m-it
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Timestamps:
00:00 - Recap
00:38 - Start Llamafactory-CLI
01:06 - Selecting Model
01:54 - Evaluation
02:04 - Chat based evaluation - Vibe checks
02:29 - Customizing model output length
02:38 - What are tokens?
02:57 - Original Gemma response
03:07 - Finetuning Motivation
03:46 - Finetuning workflow
03:56 - Finetuning Dataset
04:44 - Customizing dataset
06:26 - Checking customized dataset
06:37 - Experiment 1 - Training Settings
07:02 - Exp 1 - Organization
07:38 - Exp 1 - Dry-run
08:16 - Exp 1 - Start Finetuning
08:22 - Exp 1 - Look at Logs
09:09 - Exp 1 - Finetuning progress
09:39 - Exp 1 - Finetuning finished
10:06 - Exp 1 - Load fine-tuned model1
10:21 - Exp 1 - Evaluate finetuned model1
10:48 - Exp 1 - Identify areas of improvement
11:19 - What is an epoch?
11:54 - Experiment 2 - more epochs
12:42 - Exp 2 - Finetuning progress
13:07 - Exp 2 - Continue fine tuning
14:21 - Exp 2 - Finetuning Finished
14:42 - Exp 2 - Load finetuned model
14:56 - Exp 2 - Evaluate finetuned model2
15:00 - Exp 2 - Identify what works
15:34 - Exp 2 - Identify areas of improvement
16:43 - Model Overfitting
17:24 - Finetuning Takeaways
18:39 - Welcome to the club
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Attributions:
Stack PNGs by Vecteezy: https://www.vecteezy.com/free-png/stack
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Видео LLM Fine-tuning - No-code workflow using Llama Factory канала Noob ML Dude