[whatmodel.sh](../../README.md) / Qwen2-Math 72B Instruct
# Qwen2-Math 72B Instruct
Alibaba · released 2024-08 · 72.71B params · tongyi-qianwen
**score 50.6** ±1.2 · #356 of 732 · from 4 benchmark results across 2 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Qwen2-Math 72B Instruct among every model on that task or test, worst to best.
task vs the others rank counts tests
math ▁▁▃▅█●▄█▄▂▁▁ #245 of 431 37% 3
math · #245 of 431 · 37% 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) 40.1% ▃·····▃··█▅● beats 7/8 hard · avg 34% 16% at its level
GSM8K (maker) 96.7% ▄···█▄▄·▄·▄● beats 8/8 very easy · avg 92% 13% sharp · maxed out
MATH (subset unspecified) (maker) 84.0% ▃····▃·▅·▅·● beats 7/8 easy · avg 74% 8.4% at its level
other tests (1)
test result vs the others in its run difficulty counts why
LiveBench 2024-07-26 34.2% ▁▁▁▂▂▃▂█●▃▅▃ beats 35/61 medium · avg 38% 62% at its level
source results share of the score
LiveBench model judgments 1 62% ▇▇▇▇▇▇▇
Qwen (submitted / maker-reported) 3 37% ▇▇▇▇
prior: pull toward the middle 1% ▇
weighted average = 50.6
Licences: LiveBench model judgments ([Apache-2.0 (stated in the LiveBench datasheet)](https://www.apache.org/licenses/LICENSE-2.0))
## 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-Math 72B Instruct (Q4_K_M, needs ~47.4 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Qwen2-Math-72B-Instruct-GGUF/tree/424eb4ccda3504061d2ec8ce2e49fb7ccaceb00b))
```sh
llama cli -hf lmstudio-community/Qwen2-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 lmstudio-community/Qwen2-Math-72B-Instruct-GGUF:Q4_K_M
```
Other versions: IQ2_M (~29.3 GB) `llama cli -hf lmstudio-community/Qwen2-Math-72B-Instruct-GGUF:IQ2_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-Math 72B Instruct (Q4_K_M, needs ~47.4 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Qwen2-Math-72B-Instruct-GGUF/tree/424eb4ccda3504061d2ec8ce2e49fb7ccaceb00b))
```sh
lms get https://huggingface.co/lmstudio-community/Qwen2-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: IQ2_M (~29.3 GB) `lms get https://huggingface.co/lmstudio-community/Qwen2-Math-72B-Instruct-GGUF@IQ2_M -y` · 4bit (~40.9 GB, Mac only) `lms get https://huggingface.co/mlx-community/Qwen2-Math-72B-Instruct-4bit -y` · 8bit (~77.3 GB, Mac only) `lms get https://huggingface.co/mlx-community/Qwen2-Math-72B-Instruct-8bit -y`
### Ollama
1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download)
```sh
# Linux
curl -fsSL https://ollama.com/install.sh | sh
```
2. Run Qwen2-Math 72B Instruct (Q4_0, needs ~41 GB, from [ollama.com](https://ollama.com/library/qwen2-math/tags))
```sh
ollama run qwen2-math:72b
```
### 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-Math 72B Instruct (4bit, needs ~40.9 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/Qwen2-Math-72B-Instruct-4bit/tree/b8a475ac36f7178a875ea0f1d44c27cec7b54ba0))
```sh
mlx_lm.chat --model mlx-community/Qwen2-Math-72B-Instruct-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/Qwen2-Math-72B-Instruct-4bit
```
Other versions: 8bit (~77.3 GB, Mac only) `mlx_lm.chat --model mlx-community/Qwen2-Math-72B-Instruct-8bit`
Model repository and documentation: [huggingface.co/Qwen](https://huggingface.co/Qwen/Qwen2-Math-72B-Instruct)
[interactive version](https://whatmodel.sh/model/qwen2-math-72b) · [JSON](https://whatmodel.sh/api?format=json&all=1)