Learn to fine-tune Llama 3.2 1B language model for sentiment analysis in mental health applications through a detailed video tutorial that demonstrates the complete process using torchtune on a single GPU. Master essential steps including Google Colab setup, data preprocessing, dataset creation, model evaluation, and fine-tuning configuration. Explore how to work with the mental health sentiment dataset, evaluate the untrained model's performance, configure training parameters, and analyze training metrics. Follow along to upload the trained model to HuggingFace Hub and assess its final performance. Perfect for developers and machine learning practitioners looking to implement practical LLM fine-tuning with limited computational resources.
Fine-tuning Llama 3.2 for Mental Health Sentiment Analysis Using TorchTune
Overview
Syllabus
- Welcome
- Text tutorial on MLExpert.io
- What is torchtune?
- Google Colab setup
- Data overview & preprocessing
- Create dataset
- Download Llama 3.2
- Untrained model evaluation
- Fine-tuning configuration
- Training metrics
- Upload trained model to HuggingFace Hub
- Trained model evaluation
- Conclusion
Taught by
Venelin Valkov
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