huggingface

huggingface / post-training-takehome

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Hugging Face's take home challenge for post-training internships, now open for you to try!

45
2
100% credibility
Found Apr 28, 2026 at 45 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
AI Summary

This GitHub repository hosts a take-home exercise for Hugging Face post-training internship applicants, guiding them through generating and selecting the best AI solutions to math problems using multiple sampling and scoring techniques.

How It Works

1
🔍 Discover the Challenge

You stumble upon this fun exercise while exploring Hugging Face internship opportunities.

2
📖 Read the Guide

You dive into simple instructions on boosting AI's math skills by trying multiple answers and picking the best.

3
🧮 Choose Math Puzzles

You handpick 20 easy math problems to experiment with.

4
🤖 Try Single Answers

You ask a smart AI helper for one solution to each puzzle and check how well it does.

5
🎯 Generate Many Tries

You create lots of different solutions for each puzzle and rate them using a helpful scoring tool.

6
🏆 Pick Winners

You group similar answers, add up their scores, and choose the top one for each problem.

7
📊 Create Pictures

You make colorful charts showing how much better the multiple tries worked compared to single ones.

🎉 Share Your Story

You package your results with explanations and share them, impressing the Hugging Face team with your skills.

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AI-Generated Review

What is post-training-takehome?

This Hugging Face GitHub repo lays out a take-home challenge for post-training internships, tasking you with boosting small LLMs like Qwen2.5-1.5B on math problems from the MATH-500 dataset. You generate greedy baselines, sample N=16 solutions, score them via Skywork's process reward model using Transformers, group by final answers with weighted selection for Best-of-N accuracy, then push results to the Hub with plots. It's Python-based in the Hugging Face ecosystem, runnable on a T4 GPU, solving the pain of weak math reasoning in compact models.

Why is it gaining traction?

Unlike generic LLM tutorials, it's Hugging Face's real internship test, inspired by o1-style test-time compute papers, with agents like ML Intern already tackling it publicly. Developers dig the hands-on flow: prompt chaining, PRM scoring via last-step logits, dataset creation—no fluff, just measurable gains over greedy decoding. Ties into Hugging Face tools like Accelerate, PEFT, and Spaces for quick iteration.

Who should use this?

ML engineers prepping for Hugging Face roles or post-training gigs, LLM prompt specialists honing reward model integration on benchmarks, and agent builders experimenting with Best-of-N for math-heavy apps like Whisper or Kolors virtual try-on pipelines.

Verdict

Grab it if you're eyeing Hugging Face internships—solid README guides a 3-hour sprint with clear rubric—but at 45 stars and 1.0% credibility, it's raw practice, not production-ready code. Strong docs make it a low-risk skill-builder.

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