[whatmodel.sh](../../README.md) / Qwen2-Math 1.5B Instruct
# Qwen2-Math 1.5B Instruct
Alibaba · released 2024-08 · 1.54B params · apache-2.0
**score 46.2** ±1.8 · #451 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-Math 1.5B Instruct among every model on that task or test, worst to best.
task vs the others rank counts tests
math ▁▁▃▅●▆▄█▄▂▁▁ #322 of 431 99% 3
math · #322 of 431 · 99% of its score
Beats [GPT-4 (0613)](gpt-4-0613.md), [Athene-70B](athene-70b.md), [Gemma 2 27B IT](gemma-2-27b.md) here, though they rank higher overall. Trails [StarCoder2 7B](starcoder2-7b.md), [DeepSeek R1 Distill Qwen 1.5B](deepseek-r1-distill-qwen-1-5b.md), [Tulu 2 DPO 7B](tulu-2-dpo-7b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
GSM8K (maker) 84.2% ▄···●▄▄·▄·▄█ beats 1/8 very easy · avg 92% 45% sharp
Minerva MATH (maker) 29.4% ▃·····▃··●▅▅ beats 3/8 hard · avg 34% 31% at its level
MATH (subset unspecified) (maker) 69.4% ▃····▃·●·▅·█ beats 3/8 easy · avg 74% 22% at its level
source results share of the score
Qwen (submitted / maker-reported) 3 99% ▇▇▇▇▇▇▇▇▇▇▇▇
prior: pull toward the middle 1% ▇
weighted average = 46.2
## 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-Math 1.5B Instruct (Q4_K_M, needs ~1 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Qwen2-Math-1.5B-Instruct-GGUF/tree/4144109e282e281ed29034e690e4152c7d7ced82))
```sh
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)
```sh
llama serve -hf lmstudio-community/Qwen2-Math-1.5B-Instruct-GGUF:Q4_K_M
```
Other versions: Q8_0 (~1.6 GB) `llama cli -hf lmstudio-community/Qwen2-Math-1.5B-Instruct-GGUF:Q8_0`
### 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 1.5B Instruct (Q4_K_M, needs ~1 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Qwen2-Math-1.5B-Instruct-GGUF/tree/4144109e282e281ed29034e690e4152c7d7ced82))
```sh
lms get https://huggingface.co/lmstudio-community/Qwen2-Math-1.5B-Instruct-GGUF@Q4_K_M -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
Other versions: Q8_0 (~1.6 GB) `lms get https://huggingface.co/lmstudio-community/Qwen2-Math-1.5B-Instruct-GGUF@Q8_0 -y` · 4bit (~0.9 GB, Mac only) `lms get https://huggingface.co/mlx-community/Qwen2-Math-1.5B-Instruct-4bit -y` · 8bit (~1.6 GB, Mac only) `lms get https://huggingface.co/mlx-community/Qwen2-Math-1.5B-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 1.5B Instruct (Q4_0, needs ~0.935 GB, from [ollama.com](https://ollama.com/library/qwen2-math/tags))
```sh
ollama run qwen2-math:1.5b
```
### 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 1.5B Instruct (4bit, needs ~0.9 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/Qwen2-Math-1.5B-Instruct-4bit/tree/d2b2421fb2803e9c336eefc97f0180e000f9b2cc))
```sh
mlx_lm.chat --model mlx-community/Qwen2-Math-1.5B-Instruct-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/Qwen2-Math-1.5B-Instruct-4bit
```
Other versions: 8bit (~1.6 GB, Mac only) `mlx_lm.chat --model mlx-community/Qwen2-Math-1.5B-Instruct-8bit`
Model repository and documentation: [huggingface.co/Qwen](https://huggingface.co/Qwen/Qwen2-Math-1.5B-Instruct)
[interactive version](https://whatmodel.sh/model/qwen2-math-1-5b) · [JSON](https://whatmodel.sh/api?format=json&all=1)