Fine-Tuning Models and Large Language Models (LLMs) Training Course
Fine-tuning models and large language models (LLMs) is a critical process in adapting pre-trained machine learning models to specific tasks and datasets. This course delves into the techniques, tools, and best practices for fine-tuning, with a focus on practical implementations and optimization strategies to achieve high performance.
This instructor-led, live training (online or onsite) is designed for intermediate to advanced professionals who aim to customize pre-trained models for specific tasks and datasets in various sectors, including for government applications.
By the end of this training, participants will be able to:
- Understand the principles of fine-tuning and its applications.
- Prepare datasets for fine-tuning pre-trained models.
- Fine-tune large language models (LLMs) for natural language processing (NLP) tasks.
- Optimize model performance and address common challenges.
Format of the Course
- Interactive lectures and discussions.
- Extensive exercises and practice sessions.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, tailored to specific needs or sectors such as for government, please contact us to arrange.
Course Outline
Introduction to Fine-Tuning for Government
- What is fine-tuning?
- Use cases and benefits of fine-tuning for government applications
- Overview of pre-trained models and transfer learning in the context of public sector operations
Preparing for Fine-Tuning for Government
- Collecting and cleaning datasets for government use
- Understanding task-specific data requirements for public sector tasks
- Exploratory data analysis and preprocessing for government applications
Fine-Tuning Techniques for Government
- Transfer learning and feature extraction for government models
- Fine-tuning transformers with Hugging Face for government use cases
- Fine-tuning for supervised vs unsupervised tasks in the public sector
Fine-Tuning Large Language Models (LLMs) for Government
- Adapting LLMs for NLP tasks relevant to government operations, such as text classification and summarization
- Training LLMs with custom datasets from government sources
- Controlling LLM behavior with prompt engineering for government applications
Optimization and Evaluation for Government
- Hyperparameter tuning for government models
- Evaluating model performance in public sector contexts
- Addressing overfitting and underfitting in government fine-tuning efforts
Scaling Fine-Tuning Efforts for Government
- Fine-tuning on distributed systems for government operations
- Leveraging cloud-based solutions for scalability in the public sector
- Case studies: Large-scale fine-tuning projects for government agencies
Best Practices and Challenges for Government
- Best practices for successful fine-tuning in government applications
- Common challenges and troubleshooting in government fine-tuning efforts
- Ethical considerations in fine-tuning AI models for government use
Advanced Topics (Optional) for Government
- Fine-tuning multi-modal models for government tasks
- Zero-shot and few-shot learning for public sector applications
- Exploring LoRA (Low-Rank Adaptation) techniques in government fine-tuning
Summary and Next Steps for Government
Requirements
- Comprehensive knowledge of machine learning principles
- Proficiency in Python programming
- Understanding of pre-trained models and their practical applications
Audience for Government
- Data scientists
- Machine learning engineers
- AI researchers
Runs with a minimum of 4 + people. For 1-to-1 or private group training, request a quote.
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