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MiniCPM-Math-harness

built around a 2.5B math model that fits on one GPU or a laptop.

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This is 1 of 65 launches in llm evaluation and deployment tools — see how it stacks up on momentum and crowding →

175 other launches read as similar to this one →

Details

External ID
1412688363
Source
GITHUB
Company
—
Product
MiniCPM-Math-harness
Website domain
github.com
Launched
Oct. 10, 2026
Cohort
—
Upvotes
64
Upvotes percentile
0.76
Tags
—

Enrichment

Niche
llm evaluation and deployment tools
Vertical
Horizontal
Function
Dev tools
Audience
Developer
AI stance
AI-native
Project type
Hobby / open-source project
Normalized one-liner
evaluation harness for lightweight math models
Manually corrected
False

Could you build this?

No This is an evaluation and benchmarking harness for small-scale math LLMs (MiniCPM-Math), requiring ML engineering expertise in model quantization, inference runtimes, and specialized math evaluation datasets.

What it would actually take: Architecture requires PyTorch, vLLM or Hugging Face Transformers, and integration with evaluation suites like lm-evaluation-harness (GSM8k, MATH). The difficult part is properly configuring zero-shot/few-shot prompting protocols, LaTeX parsing, SymPy answer verification, and optimizing GPU memory footprint for 2.5B parameter models. It demands solid machine learning engineering and mathematical NLP evaluation expertise.

Competitors

Other products that read as similar to this one — 175 launches clear the similarity bar, closest 8 shown.

Attention rank: #47 of 176 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 337 days after the earliest competitor.

Other launches for this product