Fine-Tuning Open-Source LLMs (LLaMA, Mistral, Qwen, etc.) Training Course

Introduction to Open-Source LLMs

• What are open-weight models and why they're important
• Overview of LLaMA, Mistral, Qwen, and other community models
• Use cases for private, on-premise, or secure deployments

Environment Setup and Tools

• Installing and configuring Transformers, Datasets, and PEFT libraries
• Choosing appropriate hardware for fine-tuning
• Loading pre-trained models from Hugging Face or other repositories

Data Preparation and Preprocessing

• Dataset formats (instruction tuning, chat data, text-only)
• Tokenization and sequence management
• Creating custom datasets and data loaders

Fine-Tuning Techniques

• Standard full fine-tuning vs. parameter-efficient methods
• Applying LoRA and QLoRA for efficient fine-tuning
• Using Trainer API for quick experimentation

Model Evaluation and Optimization

• Assessing fine-tuned models with generation and accuracy metrics
• Managing overfitting, generalization, and validation sets
• Performance tuning tips and logging

Deployment and Private Use

• Saving and loading models for inference
• Deploying fine-tuned models in secure enterprise environments
• On-premise vs. cloud deployment strategies

Case Studies and Use Cases

• Examples of enterprise use of LLaMA, Mistral, and Qwen
• Handling multilingual and domain-specific fine-tuning
• Discussion: Trade-offs between open and closed models

Summary and Next Steps