Fine-Tuning Open-Source LLMs (LLaMA, Mistral, Qwen, etc.) Training Course
Fine-tuning open-source large language models (LLMs) is an emerging best practice for organizations that seek to customize AI capabilities in secure, cost-efficient, and private environments for government use.
This instructor-led, live training (online or onsite) is aimed at intermediate-level machine learning practitioners and artificial intelligence developers who wish to fine-tune and deploy open-source models like LLaMA, Mistral, and Qwen for specific business or internal applications within the public sector.
By the end of this training, participants will be able to:
- Understand the ecosystem and differences between various open-source LLMs.
- Prepare datasets and fine-tuning configurations for models such as LLaMA, Mistral, and Qwen.
- Execute fine-tuning pipelines using Hugging Face Transformers and PEFT.
- Evaluate, save, and deploy fine-tuned models in secure environments aligned with government standards.
Format of the Course
- Interactive lecture and discussion.
- Comprehensive exercises and practice sessions.
- 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 Large Language Models (LLMs)
- Overview of open-weight models and their significance for government applications
- Detailed review of LLaMA, Mistral, Qwen, and other community-developed models
- Exploration of use cases for private, on-premise, or secure deployments in the public sector
Environment Setup and Tools for Government Use
- Installation and configuration of Transformers, Datasets, and PEFT libraries for government systems
- Selection of appropriate hardware for fine-tuning large language models in a government setting
- Procedures for loading pre-trained models from Hugging Face or other trusted repositories
Data Preparation and Preprocessing for Government Applications
- Understanding dataset formats, including instruction tuning, chat data, and text-only datasets
- Tokenization techniques and sequence management for government-specific data
- Steps to create custom datasets and data loaders tailored to public sector needs
Fine-Tuning Techniques for Government Use
- Comparison of standard full fine-tuning versus parameter-efficient methods in a government context
- Application of LoRA and QLoRA for efficient fine-tuning in secure environments
- Utilization of the Trainer API to facilitate rapid experimentation for government projects
Model Evaluation and Optimization for Government Use
- Methods for assessing fine-tuned models using generation and accuracy metrics relevant to public sector operations
- Strategies for managing overfitting, ensuring generalization, and utilizing validation sets in government applications
- Tips for performance tuning and logging practices specific to government use cases
Deployment and Private Use for Government Operations
- Procedures for saving and loading models for inference in secure government systems
- Best practices for deploying fine-tuned models in secure enterprise environments within the public sector
- Evaluation of on-premise versus cloud deployment strategies for government applications
Case Studies and Use Cases for Government
- Examples of how LLaMA, Mistral, and Qwen are utilized in enterprise settings, including government agencies
- Approaches to handling multilingual and domain-specific fine-tuning for public sector needs
- Discussion on the trade-offs between open-source and closed models in a government context
Summary and Next Steps for Government Applications
Requirements
- An understanding of large language models (LLMs) and their architecture for government applications
- Experience with Python and PyTorch
- Basic familiarity with the Hugging Face ecosystem
Audience
- Machine learning practitioners
- Artificial intelligence developers
Runs with a minimum of 4 + people. For 1-to-1 or private group training, request a quote.
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