[SUBMISSION] Automated Policy-based Preference Alignment using Synthetic Data Generation

[souzatharsis]

December 2024 Student Submission

See html rendered submission here for ease of review.
Accompanying Python Notebook is in the PR.

Module Completed

• Module 1: Instruction Tuning
• Module 2: Preference Alignment
• Module 3: Parameter-efficient Fine-tuning
• Module 4: Evaluation
• Module 5: Vision-language Models
• Module 6: Synthetic Datasets
• Module 7: Inference
• Module 8: Deployment

Changes Made

In this case study, we demonstrate how to use DPO to align a language model to a user-provided policy further automating the process via synthetic data generation and LLM-as-judge evaluation.

We go over a Case Study for Acme Inc., a company dedicated to democratizing access to computer science education for K-12 students. Acme Inc. is in the process of creating a chatbot named smolK-12, a small open source LLM, specifically designed for K-12 students.

We’ll explore how to align a language model with Acme Inc.’s policy to ensure its LLM-powered applications are safe and appropriate for K-12 students.

Notebooks Added/Modified

List any notebooks you've added or modified:

• Added new example in 2_preference_alignment/notebooks/smolk12
• Modified existing notebook with additional examples
• Added documentation or comments

Checklist

• I have read the module materials
• My code runs without errors
• I have pushed models and datasets to the huggingface hub
  • A K-12 preference DPO-based dataset
  • A K-12 aligned language model
• My PR is based on the december_2024 branch

Questions or Discussion Points

Add any questions you have or points you'd like to discuss:
I am particularly interested in your feedback pertaining to the points I've raised in the Discussion section around:

1. Synthetic Data Generation
2. Choice of Base Model
3. Evaluation Methodology
4. DPO Dataset Composition
5. Fine-tuning Process

Additional Notes

Any other information that might be helpful for reviewers:

This is a Case Study part of an open source book I am writing "Taming LLMs".

I would love to highlight the great smolLM work you are doing here. Hence, I would truly appreciate your feedback on the here submitted Case Study.

Cheers,
Tharsis.

[burtenshaw]

This is a really nice submission @souzatharsis. Thanks!

My first question is: Have you considered using a library like distilabel?

We're currently working on the synthetic data module and I think your use case could fit there.

[souzatharsis]

Hi @burtenshaw , thanks for the feedback!
distilabel sounds indeed an elegant way to replicate my data generation process, thanks for the recommendation.

[souzatharsis]

Looking forward to additional feedback!

[souzatharsis]

Hi, I was wondering whether there is any feedback?

Would content from this PR be helpful to others in any way?