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Duration 21 hours
Course Outline
Introduction to Fine-Tuning DeepSeek Large Language Models
- Overview of the DeepSeek model series, specifically DeepSeek-R1 and DeepSeek-V3
- Rationale for fine-tuning large language models in specialized contexts
- Comparative analysis of fine-tuning versus prompt engineering strategies
Dataset Preparation for Fine-Tuning Procedures
- curation of domain-specific datasets suitable for government operations
- Application of data preprocessing and cleansing methodologies
- Tokenization protocols and dataset formatting standards for the DeepSeek LLM
Configuration of the Fine-Tuning Environment
- Setup of GPU and TPU acceleration resources
- Integration of Hugging Face Transformers with the DeepSeek LLM framework
- Analysis of critical hyperparameters governing the fine-tuning process
Execution of DeepSeek LLM Fine-Tuning
- Implementation of supervised fine-tuning methodologies
- Utilization of LoRA (Low-Rank Adaptation) and PEFT (Parameter-Efficient Fine-Tuning) techniques
- Deployment of distributed fine-tuning processes for high-volume datasets
Assessment and Optimization of Fine-Tuned Models
- Evaluation of model performance using established metrics for government use
- Mitigation of overfitting and underfitting risks
- Enhancement of inference speed and overall model efficiency
Deployment of Fine-Tuned DeepSeek Models
- Packaging of models for secure API integration
- Incorporation of fine-tuned models into operational applications
- Scaling of deployment infrastructure across cloud and edge computing environments
Practical Applications and Use Cases
- Application of fine-tuned LLMs in finance, healthcare, and public service support
- Examination of industry-specific case studies and operational outcomes
- Ethical governance and accountability considerations for domain-specific AI
Summary and Recommended Next Steps
Requirements
- Demonstrated proficiency with machine learning and deep learning frameworks
- Competent knowledge of transformer architectures and large language models (LLMs)
- Fundamental understanding of data preprocessing workflows and model training methodologies
Intended Audience
- AI researchers conducting exploratory work on LLM fine-tuning
- Machine learning engineers responsible for developing custom AI models
- Senior developers tasked with implementing AI-driven operational solutions
