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Course Outline
Introduction to DeepSeek LLM Fine-Tuning
- Overview of DeepSeek models, such as DeepSeek-R1 and DeepSeek-V3.
- Understanding the need for fine-tuning LLMs.
- Comparison of fine-tuning versus prompt engineering.
Preparing the Dataset for Fine-Tuning
- Curating domain-specific datasets.
- Techniques for data preprocessing and cleaning.
- 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
