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Course Outline
Introduction to DeepSeek LLM Fine-Tuning
- Overview of DeepSeek models, such as DeepSeek-R1 and DeepSeek-V3.
- Comprehending the necessity of fine-tuning LLMs.
- Comparing fine-tuning with prompt engineering.
Preparing the Dataset for Fine-Tuning
- Curating domain-specific datasets.
- Techniques for data preprocessing and cleaning.
- Tokenization and dataset formatting specific to DeepSeek LLM.
Setting Up the Fine-Tuning Environment
- Configuring GPU and TPU acceleration.
- Establishing Hugging Face Transformers with DeepSeek LLM.
- Understanding the role of hyperparameters in fine-tuning.
Fine-Tuning DeepSeek LLM
- Implementing supervised fine-tuning.
- Utilizing LoRA (Low-Rank Adaptation) and PEFT (Parameter-Efficient Fine-Tuning).
- Executing distributed fine-tuning for large-scale datasets.
Evaluating and Optimizing Fine-Tuned Models
- Assessing model performance using evaluation metrics.
- Addressing overfitting and underfitting challenges.
- Optimizing inference speed and overall model efficiency.
Deploying Fine-Tuned DeepSeek Models
- Packaging models for API deployment.
- Integrating fine-tuned models into existing applications.
- Scaling deployments through cloud and edge computing.
Real-World Use Cases and Applications
- Applications of fine-tuned LLMs in finance, healthcare, and customer support.
- Case studies highlighting industry applications.
- Ethical considerations associated with domain-specific AI models.
Summary and Next Steps
Requirements
- Prior experience with machine learning and deep learning frameworks.
- Familiarity with transformers and large language models (LLMs).
- Understanding of data preprocessing and model training techniques.
Target Audience
- AI researchers investigating LLM fine-tuning.
- Machine learning engineers engaged in developing custom AI models.
- Advanced developers working on AI-driven solutions.
21 Hours
