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Duration 21 hours
Course Outline
Getting Started with DeepSeek LLM Fine-Tuning
- An overview of the DeepSeek model family, specifically DeepSeek-R1 and DeepSeek-V3.
- Exploring the business and technical rationale for fine-tuning Large Language Models.
- Evaluating fine-tuning strategies against prompt engineering approaches.
Dataset Preparation for Fine-Tuning
- Selecting and curating datasets relevant to specific industry domains.
- Applying essential data preprocessing and cleaning methods.
- Managing tokenization and formatting datasets specifically for DeepSeek LLM.
Configuring the Fine-Tuning Environment
- Setting up hardware acceleration using GPU and TPU resources.
- Integrating DeepSeek LLM within the Hugging Face Transformers ecosystem.
- Understanding and tuning key hyperparameters for the fine-tuning process.
Executing DeepSeek LLM Fine-Tuning
- Implementing supervised fine-tuning workflows.
- Leveraging parameter-efficient methods like LoRA (Low-Rank Adaptation) and PEFT (Parameter-Efficient Fine-Tuning).
- Managing distributed fine-tuning tasks for large-scale datasets.
Model Evaluation and Optimization
- Measuring model performance using industry-standard evaluation metrics.
- Addressing common issues such as overfitting and underfitting.
- Enhancing inference speed and overall model efficiency.
Deployment of Fine-Tuned DeepSeek Models
- Packaging models for secure and scalable API deployment.
- Seamlessly integrating fine-tuned models into existing application architectures.
- Scaling model deployments across cloud and edge computing environments.
Practical Applications and Industry Use Cases
- Exploring applications of fine-tuned LLMs in finance, healthcare, and customer support.
- Reviewing case studies that highlight successful industry implementations.
- Navigating the ethical implications of deploying domain-specific AI models.
Course Conclusion and Future Directions
Requirements
- Practical experience with machine learning and deep learning frameworks.
- Solid familiarity with transformer architectures and large language models (LLMs).
- A clear understanding of data preprocessing workflows and model training methodologies.
Target Audience
- AI researchers investigating LLM fine-tuning methodologies.
- Machine learning engineers focused on developing custom AI models.
- Senior developers tasked with implementing AI-driven solutions.
