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Course Outline
Overview of DeepSeek Large Language Model Fine-Tuning
- Examination of DeepSeek model architectures, including DeepSeek-R1 and DeepSeek-V3
- Rationale for fine-tuning large language models within federal workflows
- Distinction between fine-tuning methodologies and prompt engineering approaches
Dataset Preparation for Fine-Ting Processes
- Acquisition and curation of domain-specific data sets
- Techniques for data preprocessing and quality assurance
- Tokenization strategies and dataset formatting standards for DeepSeek LLM integration
Configuration of the Fine-Tuning Environment
- Allocation and configuration of GPU and TPU computing resources
- Implementation of Hugging Face Transformers library with DeepSeek LLM components
- Definition and management of hyperparameters for optimal model training
Execution of DeepSeek LLM Fine-Tuning
- Implementation of supervised fine-tuning protocols
- Utilization of Low-Rank Adaptation (LoRA) and Parameter-Efficient Fine-Tuning (PEFT) techniques to reduce computational overhead for government systems
- Execution of distributed training across large-scale data sets
Performance Evaluation and Optimization of Fine-Tuned Models
- Assessment of model accuracy using established evaluation metrics
- Identification and mitigation of overfitting and underfitting conditions
- Enhancement of inference latency and overall model efficiency for operational readiness
Deployment of Fine-Tuned DeepSeek Models in Production Environments
- Preparation of models for API-based service delivery
- Integration of fine-tuned models into government application ecosystems
- Scaling infrastructure through cloud and edge computing capabilities to support high-volume requests
Application Scenarios and Sector-Specific Use Cases
- Deployment of fine-tuned LLMs in finance, healthcare, and customer service sectors for government stakeholders
- Analysis of industry case studies demonstrating operational efficacy
- Review of ethical standards and compliance requirements for domain-specific AI models
Executive Summary and Future Strategic Directions
Requirements
- Demonstrated proficiency in machine learning and deep learning frameworks
- Knowledge of transformer architectures and large language model (LLM) applications
- Competence in data preprocessing and model training methodologies
Audience
- AI researchers investigating LLM fine-tuning for government
- Machine learning engineers designing custom AI models
- Senior developers deploying AI-driven solutions
21 Hours
