whatmodel.sh / Qwen2-Math 1.5B Instruct

Qwen2-Math 1.5B Instruct

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Qwen2-Math 1.5B Instruct is an open-weights language model from Alibaba, released August 2024. Its published size is 1.54B parameters and a 4k-token context window. Licence: apache-2.0.

Catalogue identity notes

Qwen/Qwen2-Math-1.5B-Instruct @19bd485 (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 1,543,714,304 parameters (Qwen/Qwen2-Math-1.5B-Instruct @19bd485).

44.9±2.0#446 of 719 LLMsfrom 3 benchmark results across 1 source

$ how it compares

The LLMs ranked either side of Qwen2-Math 1.5B Instruct, out of 828 with a score, then more from Alibaba.

#modelscorewhereRAMreleased
532Zephyr ORPO 141B-A35B v0.1 Hugging Face45±2local~56GB2024-04
533Phi-3-medium-4k-instruct Microsoft45±1local~9.8GB2024-05
534Qwen2-Math 1.5B Instruct Alibaba45±2local~2.4GB2024-08
535Mistral Small (2402) Mistral45±1comparison only2024-02
536Llama 3.1 8B Instruct Meta45±0api $0.22/M +local~6.2GB2024-07
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▁▂▃▅▆▅▄█▄▁▁▁ #293 of 398 · 98% of its score · 3 tests

Beats GPT-4 (0613), Athene-70B, Gemma 2 27B IT here, though they rank higher overall. Trails Gemma 7B (base), DeepSeek R1 Distill Qwen 1.5B, Phi-2 (2.7B) here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
GSM8K lab84.2% ✎▄···█▄▄·▄·▄█ beats 1/8very easy · avg 92%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 42%sharp
Minerva MATH lab29.4% ✎▃·····▃··█▅▅ beats 3/8medium · avg 37%▇▇▇▇▇▇▇▇▇▇▇▇▇ 32%at its level
MATH (subset unspecified) lab69.4% ✎▃····▃·▅·▅·█ beats 3/8easy · 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 1.5B Instruct · needs ~1 GB · from huggingface.co/lmstudio-community
    chat in the terminal
    llama cli -hf lmstudio-community/Qwen2-Math-1.5B-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-1.5B-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 1.5B Instruct · needs ~1 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-1.5B-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 1.5B Instruct · needs ~0.935 GB · from ollama.com
    chat in the terminal
    ollama run qwen2-math:1.5b

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 1.5B Instruct · needs ~0.9 GB, Mac only · from huggingface.co/mlx-community
    chat in the terminal
    mlx_lm.chat --model mlx-community/Qwen2-Math-1.5B-Instruct-4bit
  3. Or start an OpenAI-compatible server at http://localhost:8080/v1
    mlx_lm.server --model mlx-community/Qwen2-Math-1.5B-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= 44.9

$ 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