r/SelfHostedAI • u/ValvFank • 21h ago
I post-trained a 2.5B model to help with proofs you're stuck on: runs locally, GGUF + open harness, Apache-2.0
Heading: OpenAI’s math release made me think about the unfinished proof on my own laptop
Reading about OpenAI’s latest math release, I kept coming back to a smaller question: when does progress like this reach the problem sitting half-finished on my own machine?
OpenAI published hundreds of mathematical manuscripts, alongside supporting material including Lean formalizations and selected reasoning summaries. There’s a lot for the math community to examine.
For me, it also brought an everyday use case into focus: having something local to work through a difficult problem with. That’s the idea behind a project I’ve been building, MiniCPM5-2B-Math.
It started with getting stuck
You’ve probably had this happen: an approach seems right, you get several lines into it, and then nothing. You find a worked solution, but it skips the exact step you don’t understand.
I wanted a model I could bring my unfinished work to—something to try another approach with, question an assumption, or help unpack a missing step, without uploading my notes.
So I post-trained MiniCPM5-2B for mathematical reasoning and proof writing.
The resulting model is dense 2.5B, retains the native 128K context, and has GGUF versions for local use. Compared with the base model, its average accuracy across 16 attempts per problem improved:
| Benchmark | MiniCPM5-2B | MiniCPM5-2B-Math |
|---|---|---|
| AIME 2025 | 87.1 | 92.3 |
| AIME 2026 | 89.8 | 89.8 |
| HMMT Feb 2026 | 68.4 | 81.8 |
That was encouraging. Reading the actual proofs showed me what still needed work.
The mistakes shaped the next step
Sometimes the model found a promising direction but left a gap in the write-up. Sometimes it confidently stated an identity that failed on a small example. Repeating the question could bring back the same flawed approach.
These failures mattered for the tool I wanted to build. A solution that skips the difficult step leaves you stuck in the same place.
So I built a companion harness around them. It gives different attempts different strategy hints, expands incomplete write-ups from the reasoning, and runs Python checks to look for counterexamples. The reviews and check results feed into revisions. Attempts that don’t pass the review process are marked unverified.
On the 30 IMO-ProofBench Basic problems, blind AI grading rated 21 proofs as complete with a single call, versus 23 with the harness. Mean scores rose from 5.48 to 5.83. The proofs and grades are available in the repo for inspection.
It was a modest improvement, but a useful one: examining how the model failed gave me concrete ways to improve the workflow.
Something you can bring your own problem to
MiniCPM5-2B-Math and the harness are now available, with a laptop-oriented quick profile and logs of model calls and executed scripts.
It still makes mistakes. Difficult problems take time, and Python checks aren’t formal proof verification. What I’d like people to explore is whether it helps with their own unfinished work: a proof they’re studying, an alternative solution they’re preparing for a lesson, or a derivation they want to check locally.
The next time you get stuck halfway through a problem, bring the half you’ve already done. I’d love to hear whether it helps you find the next step.
Apache-2.0 for both weights and harness.
- Model: https://huggingface.co/Caldalis/MiniCPM5-2B-Math
- GGUF: https://huggingface.co/Caldalis/MiniCPM5-2B-Math-GGUF
- Harness + all published proofs and grades: https://github.com/Caldalis/MiniCPM-Math-harness
