Fine-Tuning Models and Large Language Models (LLMs) Training Course
Fine-tuning models and Large Language Models (LLMs) constitutes a pivotal process in adapting pre-trained machine learning architectures to particular tasks and datasets. This curriculum delves into the methodologies, tools, and industry best practices for fine-tuning, with a strong emphasis on practical execution and optimization strategies to attain superior performance.
Presented as an instructor-led, live session (available online or onsite), this program is tailored for professionals at intermediate to advanced proficiency levels who seek to tailor pre-trained models for specialized applications and data.
Upon completion of this training, participants will be equipped to:
- Grasp the foundational principles of fine-tuning and its practical applications.
- Effectively prepare datasets for fine-tuning pre-trained models.
- Fine-tune Large Language Models (LLMs) specifically for Natural Language Processing (NLP) tasks.
- Optimize model performance and resolve common technical challenges.
Course Format
- Engaging lectures coupled with interactive discussions.
- Extensive exercises and practical practice sessions.
- Hands-on implementation within a live laboratory environment.
Customization Options
- For customized training requirements, please reach out to us to arrange the schedule.
Course Outline
Introduction to Fine-Tuning
- Defining fine-tuning
- Use cases and advantages of fine-tuning
- Overview of pre-trained models and transfer learning
Preparing for Fine-Tuning
- Collecting and cleaning datasets
- Comprehending task-specific data requirements
- Exploratory data analysis and preprocessing
Fine-Tuning Techniques
- Transfer learning and feature extraction
- Fine-tuning transformers using Hugging Face
- Fine-tuning for supervised versus unsupervised tasks
Fine-Tuning Large Language Models (LLMs)
- Adapting LLMs for NLP tasks (e.g., text classification, summarization)
- Training LLMs with custom datasets
- Controlling LLM behavior through prompt engineering
Optimization and Evaluation
- Hyperparameter tuning
- Evaluating model performance
- Addressing overfitting and underfitting
Scaling Fine-Tuning Efforts
- Fine-tuning on distributed systems
- Leveraging cloud-based solutions for scalability
- Case studies: Large-scale fine-tuning projects
Best Practices and Challenges
- Best practices for successful fine-tuning
- Common challenges and troubleshooting strategies
- Ethical considerations in fine-tuning AI models
Advanced Topics (Optional)
- Fine-tuning multi-modal models
- Zero-shot and few-shot learning
- Exploring LoRA (Low-Rank Adaptation) techniques
Summary and Next Steps
Requirements
- Solid understanding of machine learning fundamentals
- Proficiency in Python programming
- Familiarity with pre-trained models and their respective applications
Audience
- Data scientists
- Machine learning engineers
- AI researchers
Open Training Courses require 5+ participants.
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