Kimina-Autoformalizer-7B AI Solving Theorems Like a Human Mathematician



AI Summary

This video demonstrates the local installation and testing of Kimina-Autoformalizer-7B, an AI model designed to solve mathematical theorems using Lean 4 formal language. The presenter, Fahd Mirza, explores whether machine learning models can truly reason like human mathematicians.

Key Points:

Model Overview:

  • Kimina prover achieves 80% pass rate on mini F2F benchmark
  • Available in different versions including distillation and auto-formalization models
  • Open-sourced on Hugging Face
  • Based on whole proof generation enhanced by reinforcement learning
  • Comes in 72B and 7B parameter versions with 32k token context length
  • Uses “formal reasoning pattern” to bridge formal verification and informal mathematical intuition

Technical Setup:

  • Tested on Ubuntu with Nvidia RTX 6000 (48GB VRAM)
  • Uses vLLM inference engine with Text Generation Web UI
  • Model consumes over 15GB of VRAM
  • Served locally on port 7860

Testing Results:
The video demonstrates three mathematical proof attempts:

  1. Basic Triangle Theorem: Successfully proved that sum of interior angles of a triangle is 180°
  2. Triangle Inequality: Generated concise and precise Lean 4 code for the theorem
  3. Cauchy-Schwarz Inequality: Initial attempt was underwhelming, but after adjusting hyperparameters (temperature, min P, top P), produced much better results
  4. Galois Theory: Successfully handled a fundamental mathematical theorem problem

Notable Observations:

  • Model outputs often end with “by sorry” (mentioned in official documentation)
  • Generates concise and mathematically precise code
  • Performance improves with proper hyperparameter tuning
  • Demonstrates capability to handle both simple and complex mathematical theorems
  • Successfully follows prompt templates for auto-formalization tasks

Sponsors: Video sponsored by EigentBOT (deployment of personalized knowledge bots) and Vast Compute (GPU rental with 50% discount available)

The demonstration shows promising results for AI-driven formal mathematical reasoning, though with some quirks in output formatting.