[whatmodel.sh](../../README.md) / Qwen2.5-Math 1.5B Instruct
# Qwen2.5-Math 1.5B Instruct
Alibaba · released 2024-09 · 1.54B params · apache-2.0
**score 47.1** ±1.8 · #435 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 1.5B Instruct among every model on that task or test, worst to best.
task vs the others rank counts tests
math ▁▁▃▅●▆▄█▄▂▁▁ #302 of 431 98% 3
math · #302 of 431 · 98% of its score
Beats [Molmo 2 8B](molmo-2-8b.md), [DeepSeek-Coder-V2 Instruct (0724)](deepseek-coder-v2-0724.md), [Llama 3.1 Nemotron 70B Instruct](llama-3-1-nemotron-70b.md) here, though they rank higher overall. Trails [DeepSeek R1 Distill Qwen 1.5B](deepseek-r1-distill-qwen-1-5b.md), [Gemma 7B (base)](gemma-7b-base.md), [Mistral 7B v0.1 (base)](mistral-7b-v0-1.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
GSM8K (maker) 84.8% ▄···●▄▄·▄·▄█ beats 2/8 very easy · avg 92% 43% sharp
Minerva MATH (maker) 29.4% ▃·····▃··●▅▅ beats 2/8 hard · avg 34% 33% at its level
MATH (subset unspecified) (maker) 75.8% ▃····▃·▅·●·█ beats 5/8 easy · avg 74% 22% at its level
source results share of the score
Qwen (submitted / maker-reported) 3 98% ▇▇▇▇▇▇▇▇▇▇▇▇
prior: pull toward the middle 2% ▇
weighted average = 47.1
## 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 1.5B Instruct (Q4_K_M, needs ~0.99 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/Qwen2.5-Math-1.5B-Instruct-GGUF/tree/951ed2aea09c43e331c612e74d83e4a23ca98e3b))
```sh
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)
```sh
llama serve -hf bartowski/Qwen2.5-Math-1.5B-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 1.5B Instruct (Q4_K_M, needs ~0.99 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/Qwen2.5-Math-1.5B-Instruct-GGUF/tree/951ed2aea09c43e331c612e74d83e4a23ca98e3b))
```sh
lms get https://huggingface.co/bartowski/Qwen2.5-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: 4bit (~0.9 GB, Mac only) `lms get https://huggingface.co/mlx-community/Qwen2.5-Math-1.5B-Instruct-4bit -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 Qwen2.5-Math 1.5B Instruct (Q4_K_M, needs ~0.99 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/Qwen2.5-Math-1.5B-Instruct-GGUF/tree/951ed2aea09c43e331c612e74d83e4a23ca98e3b))
```sh
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.
```sh
# with uv
uv tool install mlx-lm
# or
pip install mlx-lm
```
2. Run Qwen2.5-Math 1.5B Instruct (4bit, needs ~0.9 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/Qwen2.5-Math-1.5B-Instruct-4bit/tree/ab004e134e47042c3db99d1721371626b520ac73))
```sh
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
```sh
mlx_lm.server --model mlx-community/Qwen2.5-Math-1.5B-Instruct-4bit
```
Model repository and documentation: [huggingface.co/Qwen](https://huggingface.co/Qwen/Qwen2.5-Math-1.5B-Instruct)
[interactive version](https://whatmodel.sh/model/qwen2-5-math-1-5b) · [JSON](https://whatmodel.sh/api?format=json&all=1)