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Qwen2-Math 7B Instruct

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

Catalogue identity notes

Qwen/Qwen2-Math-7B-Instruct @26843d3 (created 2024-08-08, licence apache-2.0). Math-specialised Qwen2 instruct model (English). Lab rows from the Qwen2.5-Math report Table 3 (rule b; with the catalogued Qwen2-1.5B/7B/72B-Instruct columns).

Model information sources

Release date ↗ · Weights licence ↗

HF safetensors metadata 7,615,616,512 parameters (Qwen/Qwen2-Math-7B-Instruct @26843d3).

48.6±2.1#371 of 719 LLMsfrom 3 benchmark results across 1 source

$ how it compares

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

#modelscorewhereRAMreleased
442Mixtral 8x22B Instruct v0.1 Mistral49±1local~56GB2024-04
443Yi-Coder 9B Chat 01.AI49±1local~6.8GB2024-08
444Qwen2-Math 7B Instruct Alibaba49±2local~6GB2024-08
445Magicoder-S-DS 6.7B UIUC (ise-uiuc)49±1local~5.8GB2023-12
446InternLM2.5 7B Chat Shanghai AI Laboratory (InternLM)49±2local~6.1GB2024-06
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▁▂▃▅▆▅▄█▄▁▁▁ #271 of 398 · 98% of its score · 3 tests

Beats Molmo 2 8B, DeepSeek-Coder-V2 Instruct (0724), Llama 3.1 70B Instruct here, though they rank higher overall. Trails DeepSeek R1 Distill Qwen 1.5B, Mixtral 8x7B v0.1 (base), InternVL2-4B here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
Minerva MATH lab34.6% ✎▃·····▃··█▅▅ beats 5/8medium · avg 37%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 40%at its level
GSM8K lab89.9% ✎▄···█▄▄·▄·▄█ beats 4/8very easy · avg 92%▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 35%sharp
MATH (subset unspecified) lab75.1% ✎▃····▃·▅·▅·█ beats 4/8easy · avg 76%▇▇▇▇▇▇▇▇▇ 24%at its level

$ where to run it

llama.cpp · recommended

version

  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-Math 7B Instruct · needs ~4.7 GB · from huggingface.co/lmstudio-community
    chat in the terminal
    llama cli -hf lmstudio-community/Qwen2-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 lmstudio-community/Qwen2-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-Math 7B Instruct · needs ~4.7 GB · from huggingface.co/lmstudio-community

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/lmstudio-community/Qwen2-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-Math 7B Instruct · needs ~4.4 GB · from ollama.com
    chat in the terminal
    ollama run qwen2-math:7b

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-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-Math-7B-Instruct-4bit
  3. Or start an OpenAI-compatible server at http://localhost:8080/v1
    mlx_lm.server --model mlx-community/Qwen2-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= 48.6

$ 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