Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
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
