Fine-Tuning DeepSeek LLM for Custom AI Models

• Experience with machine learning and deep learning frameworks
• Familiarity with transformers and large language models (LLMs)
• Understanding of data preprocessing and model training techniques

Audience

• AI researchers exploring LLM fine-tuning
• Machine learning engineers developing custom AI models
• Advanced developers implementing AI-driven solutions

DeepSeek LLM, including models like DeepSeek-R1 and DeepSeek-V3, provides a powerful foundation for building AI applications. Fine-tuning these models on domain-specific datasets enables the creation of specialized AI solutions tailored to business needs.

This instructor-led, live training (online or onsite) is aimed at advanced-level AI researchers, machine learning engineers, and developers who wish to fine-tune DeepSeek LLM models to create specialized AI applications tailored to specific industries, domains, or business needs.

By the end of this training, participants will be able to:

• Understand the architecture and capabilities of DeepSeek models, including DeepSeek-R1 and DeepSeek-V3.
• Prepare datasets and preprocess data for fine-tuning.
• Fine-tune DeepSeek LLM for domain-specific applications.
• Optimize and deploy fine-tuned models efficiently.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction to DeepSeek LLM Fine-Tuning

• Overview of DeepSeek models, e.g. DeepSeek-R1 and DeepSeek-V3
• Understanding the need for fine-tuning LLMs
• Comparison of fine-tuning vs. prompt engineering

Preparing the Dataset for Fine-Tuning

• Curating domain-specific datasets
• Data preprocessing and cleaning techniques
• Tokenization and dataset formatting for DeepSeek LLM

Setting Up the Fine-Tuning Environment

• Configuring GPU and TPU acceleration
• Setting up Hugging Face Transformers with DeepSeek LLM
• Understanding hyperparameters for fine-tuning

Fine-Tuning DeepSeek LLM

• Implementing supervised fine-tuning
• Using LoRA (Low-Rank Adaptation) and PEFT (Parameter-Efficient Fine-Tuning)
• Running distributed fine-tuning for large-scale datasets

Evaluating and Optimizing Fine-Tuned Models

• Assessing model performance with evaluation metrics
• Handling overfitting and underfitting
• Optimizing inference speed and model efficiency

Deploying Fine-Tuned DeepSeek Models

• Packaging models for API deployment
• Integrating fine-tuned models into applications
• Scaling deployments with cloud and edge computing

Real-World Use Cases and Applications

• Fine-tuned LLMs for finance, healthcare, and customer support
• Case studies of industry applications
• Ethical considerations in domain-specific AI models

Summary and Next Steps