whatmodel.sh / Qwen2.5-Math 1.5B Instruct

Qwen2.5-Math 1.5B Instruct

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Qwen2.5-Math 1.5B Instruct is an open-weights language model from Alibaba, released September 2024. Its published size is 1.54B parameters and a 4k-token context window. Licence: apache-2.0.

Catalogue identity notes

Qwen/Qwen2.5-Math-1.5B-Instruct @aafeb0f (created 2024-09-16, 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 1,543,714,304 parameters (Qwen/Qwen2.5-Math-1.5B-Instruct @aafeb0f).

45.9±2.0#426 of 719 LLMsfrom 3 benchmark results across 1 source

$ how it compares

The LLMs ranked either side of Qwen2.5-Math 1.5B Instruct, out of 828 with a score, then more from Alibaba.

#modelscorewhereRAMreleased
505OpenChat 3.5 0106 OpenChat46±1local~6.1GB2024-01
506Qwen2-VL 7B Instruct Alibaba46±1local~6.4GB2024-08
507Qwen2.5-Math 1.5B Instruct Alibaba46±2local~2.4GB2024-09
508Yi 1.5 34B Chat 01.AI46±1local~22GB2024-05
509DBRX Instruct Databricks46±1local~85GB2024-03
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▁▂▃▅▆▅▄█▄▁▁▁ #278 of 398 · 98% of its score · 3 tests

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

testresultvs the others in its rundifficultycountswhy
GSM8K lab84.8% ✎▄···█▄▄·▄·▄█ beats 2/8very easy · avg 92%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 40%sharp
Minerva MATH lab29.4% ✎▃·····▃··█▅▅ beats 2/8medium · avg 37%▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 34%at its level
MATH (subset unspecified) lab75.8% ✎▃····▃·▅·▅·█ beats 5/8easy · avg 76%▇▇▇▇▇▇▇▇▇▇ 24%at its level

$ 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 1.5B Instruct · needs ~0.99 GB · from huggingface.co/bartowski
    chat in the terminal
    llama cli -hf bartowski/Qwen2.5-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 bartowski/Qwen2.5-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 Qwen2.5-Math 1.5B Instruct · needs ~0.99 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-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 Qwen2.5-Math 1.5B Instruct · needs ~0.99 GB · from huggingface.co/bartowski
    chat in the terminal
    ollama run hf.co/bartowski/Qwen2.5-Math-1.5B-Instruct-GGUF:Q4_K_M

MLX (Mac)

  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 1.5B Instruct · needs ~0.9 GB, Mac only · from huggingface.co/mlx-community
    chat in the terminal
    mlx_lm.chat --model mlx-community/Qwen2.5-Math-1.5B-Instruct-4bit
  3. Or start an OpenAI-compatible server at http://localhost:8080/v1
    mlx_lm.server --model mlx-community/Qwen2.5-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= 45.9

$ 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