Fine-Tuning Open-Source LLMs (LLaMA, Mistral, Qwen, etc.) Training Course
Fine-tuning open-source LLMs has become a leading best practice for organizations aiming to customize AI capabilities within secure, cost-effective, and private environments.
This instructor-led, live training (available online or onsite) is designed for intermediate-level ML practitioners and AI developers who want to fine-tune and deploy open-weight models such as LLaMA, Mistral, and Qwen for specific business or internal applications.
By the end of this training, participants will be able to:
- Understand the ecosystem and distinctions between open-source LLMs.
- Prepare datasets and fine-tuning configurations for models like LLaMA, Mistral, and Qwen.
- Execute fine-tuning pipelines using Hugging Face Transformers and PEFT.
- Evaluate, save, and deploy fine-tuned models in secure environments.
Format of the Course
- Interactive lecture and discussion.
- Extensive exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request customized training for this course, please contact us to arrange.
Course Outline
Introduction to Open-Source LLMs
- What open-weight models are and why they matter.
- 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 versus parameter-efficient methods.
- Applying LoRA and QLoRA for efficient fine-tuning.
- Using the 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
Requirements
- An understanding of large language models (LLMs) and their architecture.
- Experience with Python and PyTorch.
- Basic familiarity with the Hugging Face ecosystem.
Audience
- ML practitioners.
- AI developers.
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