LLM Fine-Tuning using Azure

A fine-tuning guide for both OpenAI and Open-Source Large Lauguage Models on Azure.

What

Fine-Tuning, or Supervised Fine-Tuning, retrains an existing pre-trained LLM using example data, resulting in a new "custom" fine-tuned LLM that has been optimized for the provided task-specific examples.

Why

Typically, we use Fine-Tuning to:

• improve LLM performance on specific tasks.
• introduce information that wasn't well represented by the base LLM model.

Good use cases include:

• steering the LLM outputs in a specific style or tone.
• too long or complex prompts to fit into the LLM prompt window.

When

You may consider Fine-Tuning when:

• you have tried Prompt Engineering and RAG approaches.
• latency is critically important to the use case.
• high accuracy is required to meet the customer requirement.
• you have thousands of high-quality samples with ground-truth data.
• you have clear evaluation metrics to benchmark fine-tuned models.

Learning Path

Lab 1: LLM Fine-Tuning via Dashboards

• Lab 1.1: Fine-Tuning GPT Models (1h duration)
• Lab 1.2: Fine-Tuning Llama2 Models (1h duration)

Lab 2: LLM Fine-Tuning via Python SDK

• Lab 2.1: Fine-Tuning GPT Models (2h duration)
• Lab 2.2: Fine-Tuning Llama2 Models (2h duration)

Contributing

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This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

Trademarks

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Code of Conduct

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

License

Copyright (c) Microsoft Corporation. All rights reserved.

Licensed under the MIT license.

Reporting Security Issues

Reporting Security Issues