Fine-Tuning Open-Source LLMs (LLaMA, Mistral, Qwen, etc.) Training Course
Fine-tuning open-source LLMs is an emerging best practice for organizations that seek to customize AI capabilities in secure, cost-efficient, and private environments.
This instructor-led, live training (online or onsite) is aimed at intermediate-level ML practitioners and AI developers who wish to fine-tune and deploy open-weight models like 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 differences 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.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Course Outline
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
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
Runs with a minimum of 4 + people. For 1-to-1 or private group training, request a quote.
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