---

license: apache-2.0

language:

- en

- zh

library_name: gguf

pipeline_tag: text-generation

base_model: Caldalis/MiniCPM5-2B-Math

base_model_relation: quantized

tags:

- minicpm

- minicpm5

- math

- reasoning

- theorem-proving

- gguf

- llama.cpp

---

<div align="center">

<img src="https://huggingface.co/Caldalis/MiniCPM5-2B-Math/resolve/main/assets/banner.png" alt="MiniCPM5-2B-Math: a 2.5B model for math and proofs, with a proof swarm you can run yourself" width="100%" />

</div>

<p align="center">

English |

<a href="https://huggingface.co/Caldalis/MiniCPM5-2B-Math-GGUF/blob/main/README-cn.md" target="_blank">中文</a>

</p>

MiniCPM5-2B-Math-GGUF

GGUF builds of MiniCPM5-2B-Math, a 2.5B math and proof

specialist, for llama.cpp, Ollama and LM Studio. Pair it with the

MiniCPM-Math Harness to run a small proof swarm on a laptop.

| File | Size | Notes |

|---|---:|---|

| MiniCPM5-2B-Math-Q4_K_M.gguf | 1.6 GB | smallest; quality loss likely on long proofs |

| MiniCPM5-2B-Math-Q8_0.gguf | 2.7 GB | recommended: usually close to BF16, and used for our laptop tests |

| MiniCPM5-2B-Math-BF16.gguf | 5.0 GB | full precision |

Converted with llama.cpp b10964 (convert_hf_to_gguf.py, tokenizer pre-type minicpm5) and quantized with

llama-quantize. The recommended sampling settings (temperature 0.9, top-p 0.95, min-p 0, top-k off) are embedded in

the GGUF metadata, so llama.cpp uses them by default. All results on the

model card were measured with the BF16 safetensors on vLLM.

| File | SHA-256 |

|---|---|

| MiniCPM5-2B-Math-Q4_K_M.gguf | 9ff4f86fd8eb3a16b1ff6b29d86366f3c963320e44a7325e6fb90c0142a7cf0b |

| MiniCPM5-2B-Math-Q8_0.gguf | db251717a4d11b3221e7e2f1d096c3aebec94b1f25e8e24b4ebc56c185d32b9d |

| MiniCPM5-2B-Math-BF16.gguf | 0703334ee732e110ef18dee427a8736fae52fbdd8a6ce3510de0df45dcaac72c |

llama.cpp

llama-server -hf Caldalis/MiniCPM5-2B-Math-GGUF:Q8_0 --jinja -ngl 99 -c 98304 -np 4 -kvu --port 8000

• --jinja uses the embedded chat template (thinking mode, <think> … </think>).
• -np 4 -kvu gives 4 parallel slots that share a 96K-token KV cache: one slot per agent for the harness. Run the

harness with --profile quick against this server.

• --port 8000 is where the harness looks by default; without it, llama-server listens on 8080.
• The embedded defaults already set min_p=0; llama.cpp's usual 0.05 can encourage repetition with this model.

Ollama

ollama run hf.co/Caldalis/MiniCPM5-2B-Math-GGUF:Q8_0

Start the server with a larger context window (for example OLLAMA_CONTEXT_LENGTH=65536 ollama serve), because

proofs routinely need tens of thousands of tokens. With the harness, pass

--base-url http://127.0.0.1:11434/v1 --model hf.co/Caldalis/MiniCPM5-2B-Math-GGUF:Q8_0 --no-raw.

Recommended settings

temperature=0.9, top_p=0.95, min_p=0.0, thinking enabled, and a large max_tokens. The published benchmark

numbers let every call use the full 131,072-token context: a quarter of HMMT samples think past 64K tokens, and many

of those still end with the right answer. On a laptop, 16K–32K is a practical compromise.

License

Apache-2.0 (see LICENSE).