whatmodel.sh / Qwen2.5-Math 7B Instruct

Qwen2.5-Math 7B Instruct

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Qwen2.5-Math 7B Instruct is an open-weights language model from Alibaba, released September 2024. Its published size is 7.62B parameters and a 4k-token context window. Licence: apache-2.0.

Catalogue identity notes

Qwen/Qwen2.5-Math-7B-Instruct @ef9926d (created 2024-09-19, licence apache-2.0). Math-specialised Qwen2.5 instruct model (CoT and tool-integrated reasoning). Lab rows from its own report Table 3 (rule b) with the catalogued Qwen2-Instruct columns. No ollama library tag.

Model information sources

Release date ↗ · Weights licence ↗

HF safetensors metadata 7,615,616,512 parameters (Qwen/Qwen2.5-Math-7B-Instruct @ef9926d).

52.3±2.2#311 of 719 LLMsfrom 3 benchmark results across 1 source

$ how it compares

The LLMs ranked either side of Qwen2.5-Math 7B Instruct, out of 828 with a score, then more from Alibaba.

#modelscorewhereRAMreleased
372Llama 3.1 70B Instruct Meta52±1api $0.72/M +local~27GB2024-07
373Yi-Large Preview 01.AI52±1comparison only2024-05
374Qwen2.5-Math 7B Instruct Alibaba52±2local~6GB2024-09
375LFM2.5 8B-A1B Liquid AI52±1local~6.5GB2026-05
376GPT-4 (0314) OpenAI52±1comparison only2023-03
more from Alibaba
18Qwen3.8 Max (2.4T-A95B) Alibaba78±1api $3.00/M +local~380GB2026-08
34Qwen3.8 Max (0902) Alibaba76±1api $3.00/M2026-09
37Qwen3.8-Flash-Next Alibaba75±1local~59GB2026-08

see all LLMs · find the ones that fit your machine

$ what it's good at

math▁▂▃▅▆▅▄█▄▁▁▁ #230 of 398 · 98% of its score · 3 tests

Beats GPT-4o (2024-08-06), Command A (03-2025), GPT-4o (2024-11-20) here, though they rank higher overall. Trails OpenThinker 7B, Olmo 3 7B Instruct, Tulu 2 70B here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
Minerva MATH lab37.1% ✎▃·····▃··█▅▅ beats 6/8medium · avg 37%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 48%at its level
GSM8K lab95.2% ✎▄···█▄▄·▄·▄█ beats 6/8very easy · avg 92%▇▇▇▇▇▇▇▇▇▇▇ 28%sharp · maxed out
MATH (subset unspecified) lab83.6% ✎▃····▃·▅·▅·█ beats 6/8easy · avg 76%▇▇▇▇▇▇▇▇▇ 22%typical

$ where to run it

llama.cpp · recommended

  1. Install llama.cpp · Homebrew and winget installs name the commands llama-cli and llama-server instead.
    macOS / Linux
    curl -LsSf https://llama.app/install.sh | sh
    Windows
    irm https://llama.app/install.ps1 | iex

    other ways to install ↗

  2. Run Qwen2.5-Math 7B Instruct · needs ~4.68 GB · from huggingface.co/bartowski
    chat in the terminal
    llama cli -hf bartowski/Qwen2.5-Math-7B-Instruct-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf bartowski/Qwen2.5-Math-7B-Instruct-GGUF:Q4_K_M

LM Studio

version

  1. Install LM Studio · Open the app once before using the lms command.

    download it (macOS / Windows / Linux) ↗

  2. Run Qwen2.5-Math 7B Instruct · needs ~4.68 GB · from huggingface.co/bartowski

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/bartowski/Qwen2.5-Math-7B-Instruct-GGUF@Q4_K_M -y
  4. Then run lms ls to see its name, and lms chat followed by that name

Ollama

  1. Install Ollama

    download it (macOS / Windows) ↗

    Linux
    curl -fsSL https://ollama.com/install.sh | sh
  2. Run Qwen2.5-Math 7B Instruct · needs ~4.68 GB · from huggingface.co/bartowski
    chat in the terminal
    ollama run hf.co/bartowski/Qwen2.5-Math-7B-Instruct-GGUF:Q4_K_M

MLX (Mac)

version

  1. Install MLX (Mac) · Apple silicon Macs only, text only.
    with uv
    uv tool install mlx-lm
    or
    pip install mlx-lm
  2. Run Qwen2.5-Math 7B Instruct · needs ~4.3 GB, Mac only · from huggingface.co/mlx-community
    chat in the terminal
    mlx_lm.chat --model mlx-community/Qwen2.5-Math-7B-Instruct-4bit
  3. Or start an OpenAI-compatible server at http://localhost:8080/v1
    mlx_lm.server --model mlx-community/Qwen2.5-Math-7B-Instruct-4bit

Official weights and documentation: huggingface.co/Qwen ↗

$ how the score was worked out · Qwen (Alibaba) vendor-reporte… 98%

Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its counts column above; here it's added up by who ran the tests. How it works

sourceresultsshare of the scorelicence
Qwen (Alibaba) vendor-reported benchmark scores for its own models (qwen.ai launch posts, Qwen2.5-VL and Qwen3-VL technical reports, Qwen3.8-Flash-Next card) · lab-reported3▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 98%Factual vendor-reported score (not licensed content)
prior · pull toward the middle▇ 2%
weighted average= 52.3

$ improve this page

Update model informationAdd a place to run itAdd a test resultPropose a benchmarkCorrect a benchmark scoreEdit data on GitHub

Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.

data updated · sources and licences · json