[whatmodel.sh](../../README.md) / Qwen2.5-Math 72B Instruct
# Qwen2.5-Math 72B Instruct
Alibaba · released 2024-09 · 72.71B params · qwen
**score 55.2** ±2.1 · #279 of 732 · from 3 benchmark results across 1 source · † no independently run results verified
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Qwen2.5-Math 72B Instruct among every model on that task or test, worst to best.
task vs the others rank counts tests
math ▁▁▃▅█●▄█▄▂▁▁ #237 of 431 98% 3
math · #237 of 431 · 98% of its score
Beats [GPT-4o (2024-08-06)](gpt-4o-2024-08-06.md), [Command A (03-2025)](command-a.md), [GPT-4o (2024-05-13)](gpt-4o-2024-05-13.md) here, though they rank higher overall. Trails [Gemma 7B (base)](gemma-7b-base.md), [CodeGemma 7B (v1.0)](codegemma-7b.md), [Phi-2 (2.7B)](phi-2.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Minerva MATH (maker) 44.1% ▃·····▃··█▅● beats 8/8 hard · avg 34% 53% at its level
GSM8K (maker) 95.9% ▄···█▄▄·▄·▄● beats 7/8 very easy · avg 92% 25% sharp · maxed out
MATH (subset unspecified) (maker) 85.9% ▃····▃·▅·▅·● beats 8/8 easy · avg 74% 21% typical
source results share of the score
Qwen (submitted / maker-reported) 3 98% ▇▇▇▇▇▇▇▇▇▇▇▇
prior: pull toward the middle 2% ▇
weighted average = 55.2
## How to run it
### llama.cpp (recommended)
1. Install llama.cpp. Homebrew and winget installs name the commands llama-cli and llama-server instead. [Other ways to install](https://github.com/ggml-org/llama.cpp/blob/master/docs/install.md)
```sh
# macOS / Linux
curl -LsSf https://llama.app/install.sh | sh
# Windows
irm https://llama.app/install.ps1 | iex
```
2. Run Qwen2.5-Math 72B Instruct (Q4_K_M, needs ~47.42 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/Qwen2.5-Math-72B-Instruct-GGUF/tree/71122712ff373230143d65feb785ba782b6520d4))
```sh
llama cli -hf bartowski/Qwen2.5-Math-72B-Instruct-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf bartowski/Qwen2.5-Math-72B-Instruct-GGUF:Q4_K_M
```
### LM Studio
1. Install LM Studio. Open the app once before using the lms command. [Download it (macOS / Windows / Linux)](https://lmstudio.ai/download)
2. Run Qwen2.5-Math 72B Instruct (Q4_K_M, needs ~47.42 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/Qwen2.5-Math-72B-Instruct-GGUF/tree/71122712ff373230143d65feb785ba782b6520d4))
```sh
lms get https://huggingface.co/bartowski/Qwen2.5-Math-72B-Instruct-GGUF@Q4_K_M -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
Other versions: 4bit (~40.9 GB, Mac only) `lms get https://huggingface.co/mlx-community/Qwen2.5-Math-72B-Instruct-4bit -y`
### MLX (Mac)
1. Install MLX (Mac). Apple silicon Macs only, text only.
```sh
# with uv
uv tool install mlx-lm
# or
pip install mlx-lm
```
2. Run Qwen2.5-Math 72B Instruct (4bit, needs ~40.9 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/Qwen2.5-Math-72B-Instruct-4bit/tree/55ec0cc0522c4fefbb0e04969c1a739b26b1f89a))
```sh
mlx_lm.chat --model mlx-community/Qwen2.5-Math-72B-Instruct-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/Qwen2.5-Math-72B-Instruct-4bit
```
Model repository and documentation: [huggingface.co/Qwen](https://huggingface.co/Qwen/Qwen2.5-Math-72B-Instruct)
[interactive version](https://whatmodel.sh/model/qwen2-5-math-72b) · [JSON](https://whatmodel.sh/api?format=json&all=1)