Skip to content

Latest commit

 

History

History
40 lines (30 loc) · 3.55 KB

README.md

File metadata and controls

40 lines (30 loc) · 3.55 KB

Aligning ML Models with Human Feedback

Stages of model alignment

What this repository contains:

This repository contains a collection of tutorials, best practices, and references for developers, data scientists, and machine learning professionals of all skill levels.

This repo may be of interest to you if:

If you have any questions about this repo, or need a hand:

Establishing Supervised Model Baseline

In this step, we collect labeled text data to train an initial Large Language Model (LLM), focusing on task-specific performance improvements. This stage involves gathering instructions and responses to adapt the base model to a broad range of tasks, enhancing its ability to generate accurate and contextually relevant responses. Typically this step involves:

  • Selecting baseline Foundational Model (FM) that can perform fairly well on general tasks (like GPT2 or GPT-J)
  • Generate dataset of pairs prompt input followed by response. You can manually label dataset or generate the data like it is provided in the example
  • Peform supervised model finetuning.

Gathering and Incorporating Human Feedback

This stage involves collecting comparison data to establish human preferences for the responses generated by the supervised model. By ranking multiple responses based on quality, we can train a reward model that effectively captures human preferences. This reward model plays a crucial role in reinforcement learning, optimizing the performance of the fine-tuned foundational model.

Gathering Human Feedback Tutorial - This Jupyter Notebook tutorial will guide you through the process of collecting comparison data, establishing human preferences, and incorporating this feedback into the reward model training.

Training and Assessing the Final Model with Reinforcement Learning

The training stage is a challenging process, and the final model assessment is a critical component in evaluating your model's quality. It is essential to determine whether the model adheres to the provided instructions, avoids biases, and maintains a high standard of performance.