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Course Outline
Introduction to Fine-Tuning DeepSeek LLMs
- Overview of DeepSeek models, such as DeepSeek-R1 and DeepSeek-V3
- Exploring the necessity of fine-tuning LLMs
- Analyzing the differences between fine-tuning and prompt engineering
Dataset Preparation for Fine-Tuning
- Selecting domain-specific datasets
- Techniques for data preprocessing and cleaning
- Tokenization and dataset structuring for DeepSeek LLM
Configuring the Fine-Tuning Environment
- Setting up GPU and TPU acceleration
- Integrating Hugging Face Transformers with DeepSeek LLM
- Interpreting key hyperparameters for fine-tuning
Executing DeepSeek LLM Fine-Tuning
- Applying supervised fine-tuning methods
- Leveraging LoRA (Low-Rank Adaptation) and PEFT (Parameter-Efficient Fine-Tuning)
- Conducting distributed fine-tuning for large-scale datasets
Evaluation and Optimization of Fine-Tuned Models
- Measuring model performance using evaluation metrics
- Addressing issues of overfitting and underfitting
- Enhancing inference speed and model efficiency
Deployment of Fine-Tuned DeepSeek Models
- Preparing models for API deployment
- Incorporating fine-tuned models into application stacks
- Scaling deployments across cloud and edge computing environments
Practical Use Cases and Industry Applications
- Application of fine-tuned LLMs in finance, healthcare, and customer support
- Examination of real-world industry case studies
- Ethical considerations in developing domain-specific AI models
Conclusions and Future Directions
Requirements
- Practical experience with machine learning and deep learning frameworks
- Proficiency with transformer architectures and large language models (LLMs)
- Solid understanding of data preprocessing workflows and model training methodologies
Target Audience
- AI researchers investigating LLM fine-tuning strategies
- Machine learning engineers building custom AI models
- Advanced developers deploying AI-driven solutions
21 Hours
