How-To Fine-Tune Qwen3 on Custom Dataset Step-by-Step Tutorial
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Video Summary: Fine-tuning Coen3 30 Billion Mixture of Expert Model
Overview:
The video covers the process of fine-tuning the Coen3 30 billion mixture of expert language model on a custom dataset, all within a local environment and for free.1. Introduction to Tools:
- Fine-tuning Tool: Unsloth, which allows for efficient model fine-tuning with less memory usage.
- Supports various quantization techniques.
- Optimizes performance for multiple GPU types.
2. System Requirements:
- OS: Ubuntu 22.04
- GPU: Nvidia RTX 6000 with 48 GB VRAM.
3. Setup Instructions:
- Create a virtual environment using Conda.
- Installation of required prerequisites for Unsloth.
- Model and dataset downloads.
4. Model Selection:
- Selecting models from Hugging Face repo, including Quen series.
5. Fine-tuning Process:
- Use Parameter Efficient Fine-Tuning (PEFT) with LoRA techniques, focusing on specific model layers to save computation.
- Set hyperparameters: Rank, Dropout, Memory optimization techniques.
6. Dataset Preparation:
- Load and customize dataset for fine-tuning, defining a conversational format.
- Create a combined dataset (25% reasoning, 75% chat) for balanced training.
7. Training Configuration:
- Setup fine-tuning using TRL library: define parameters for the training procedure.
- Monitor VRAM consumption during training.
8. Model Evaluation:
- Utilize model on various reasoning tasks and evaluate performance.
- Discussion on logging and monitoring training loss.
9. Saving and Uploading the Model:
- Instructions to save the fine-tuned model locally or upload to Hugging Face for future use.
- Linking to relevant code and additional resources in description.
Conclusion:
- Possibility to run in Google Colab for free.
- Encouragement to subscribe for more AI content.