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

Introduction to Open-Source LLMs

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

Environment Setup and Tools

• Installing and configuring the Transformers, Datasets, and PEFT libraries.
• Selecting 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 versus parameter-efficient methods.
• Applying LoRA and QLoRA for efficient fine-tuning.
• Using the Trainer API for rapid experimentation.

Model Evaluation and Optimization

• Assessing fine-tuned models using generation and accuracy metrics.
• Managing overfitting, generalization, and validation sets.
• Tips for performance tuning and logging.

Deployment and Private Use

• Saving and loading models for inference.
• Deploying fine-tuned models within secure enterprise environments.
• Strategies for on-premise versus cloud deployment.

Case Studies and Use Cases

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

Summary and Next Steps