Fine-Tuning DeepSeek LLM for Custom AI Models Training Course
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.
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Course Outline
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
Requirements
- 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
21 Hours
Open Training Courses require 5+ participants.
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