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.
Duration 21 hours
Course Outline
Introduction to Fine-Tuning DeepSeek LLMs
- Overview of DeepSeek models, including DeepSeek-R1 and DeepSeek-V3
- The rationale behind fine-tuning Large Language Models
- Distinguishing fine-tuning from prompt engineering techniques
Dataset Preparation for Fine-Tuning
- Selecting and curating domain-specific datasets
- Applying data preprocessing and cleaning methods
- Tokenization and formatting datasets for DeepSeek LLM integration
Establishing the Fine-Tuning Environment
- Configuring acceleration using GPUs and TPUs
- Integrating Hugging Face Transformers with DeepSeek LLM
- Key hyperparameters to consider for fine-tuning
Executing DeepSeek LLM Fine-Tuning
- Implementing supervised fine-tuning workflows
- Utilizing LoRA (Low-Rank Adaptation) and PEFT (Parameter-Efficient Fine-Tuning)
- Managing distributed fine-tuning for large-scale datasets
Model Evaluation and Optimization
- Measuring model performance using relevant evaluation metrics
- Mitigating issues related to overfitting and underfitting
- Enhancing inference speed and overall model efficiency
Deployment of Fine-Tuned DeepSeek Models
- Preparing models for API-based deployment
- Embedding fine-tuned models into existing applications
- Scaling deployments via cloud and edge computing architectures
Practical Applications and Case Studies
- Applications of fine-tuned LLMs in finance, healthcare, and customer support
- Analysis of real-world industry implementations
- Ethical implications in domain-specific AI model development
Conclusion and Future Directions
Requirements
- Practical experience with machine learning and deep learning frameworks
- Familiarity with transformers and Large Language Models (LLMs)
- Solid understanding of data preprocessing and model training methodologies
Target Audience
- AI researchers investigating LLM fine-tuning strategies
- Machine learning engineers building custom AI models
- Advanced developers integrating AI-driven solutions
