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Course Outline

Introduction to DeepSeek LLM Fine-Tuning

  • Overview of DeepSeek models, e.g., 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.
  • Data preprocessing and cleaning techniques.
  • 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

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