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Course Outline
Introduction to Fine-Tuning DeepSeek LLMs
- An overview of the DeepSeek model family, such as DeepSeek-R1 and DeepSeek-V3
- The rationale behind fine-tuning large language models
- Distinguishing between fine-tuning and prompt engineering approaches
Dataset Preparation for Fine-Tuning
- Selecting and curating domain-relevant datasets
- Techniques for data preprocessing and cleansing
- Tokenization strategies and dataset structuring for DeepSeek LLMs
Establishing the Fine-Tuning Environment
- Setting up GPU and TPU acceleration for optimal performance
- Integrating Hugging Face Transformers with DeepSeek LLMs
- Identifying key hyperparameters that influence fine-tuning outcomes
Executing DeepSeek LLM Fine-Tuning
- Applying supervised fine-tuning methods
- Leveraging LoRA (Low-Rank Adaptation) and PEFT (Parameter-Efficient Fine-Tuning)
- Managing distributed fine-tuning processes for large-scale datasets
Assessing and Refining Fine-Tuned Models
- Measuring model effectiveness using appropriate evaluation metrics
- Mitigating issues related to overfitting and underfitting
- Enhancing inference speed and overall model efficiency
Rollout of Fine-Tuned DeepSeek Models
- Preparing models for deployment via APIs
- Embedding fine-tuned models into existing application architectures
- Expanding deployment capabilities through cloud and edge computing resources
Practical Use Cases and Industry Applications
- Applying fine-tuned LLMs in finance, healthcare, and customer service sectors
- Analyzing case studies of successful industry implementations
- Navigating ethical considerations in specialized AI models
Recap and Forward-Looking Steps
Requirements
- Practical experience with machine learning and deep learning frameworks
- Familiarity with transformer architectures and large language models (LLMs)
- Solid understanding of data preprocessing workflows and model training methodologies
Intended Audience
- AI researchers investigating the nuances of LLM fine-tuning
- Machine learning engineers engineering custom AI models
- Senior developers integrating AI-driven capabilities into their products
21 Hours
