How to Fine-Tune DeepSeek R1 LLM - Step-by-Step Tutorial

Übersicht

Lehrplan

• Course Introduction
Overview of the DeepSeek R1 LLM
Course Objectives and Outcome
Prerequisites and Required Resources
• Module 1: Environment Setup
System Requirements
Installing Necessary Software and Libraries
Configuring the Development Environment
• Module 2: Cloud GPU Usage
Selecting a Cloud Provider and Service
Configuring and Launching GPU Instances
Cost Management and Optimization
• Module 3: Introduction to PEFT and LoRA
Overview of Parameter Efficient Fine-Tuning (PEFT)
Understanding Low-Rank Adaptations (LoRA)
Benefits and Applications in Fine-Tuning
• Module 4: Fine-Tuning the DeepSeek R1 LLM
Data Collection and Preparation
Applying PEFT and LoRA Techniques
Monitoring Training Progress and Adjusting Parameters
• Module 5: Running Inference with Customized Model
Exporting and Deploying the Fine-Tuned Model
Conducting Inference and Evaluation
Debugging and Optimizing Performance
• Module 6: Case Studies and Best Practices
Real-World Applications of Fine-Tuned LLMs
Troubleshooting Common Issues
Ethical Considerations and Bias Mitigation
• Course Conclusion
Recap of Key Learnings
Next Steps and Further Learning Opportunities
Feedback and Course Evaluation
• Additional Resources
Recommended Reading
Online Tools and Communities
Certification and Further Opportunities

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Fachgebiete

Computer Science