[whatmodel.sh](../../README.md) / Qwen2.5 Coder 0.5B Instruct
# Qwen2.5 Coder 0.5B Instruct
Alibaba · released 2024-11 · 0.49B params · apache-2.0
**score 38.4** ±1.1 · #590 of 732 · from 9 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 Qwen2.5 Coder 0.5B Instruct among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃●▇█▇▅▄▃▁ #410 of 469 96% 8
coding · #410 of 469 · 96% of its score
Beats [DeepSeek R1 Distill Qwen 7B](deepseek-r1-distill-qwen-7b.md), [Qwen3.5 2B](qwen3-5-2b.md), [DeepSeek R1 Distill Llama 8B](deepseek-r1-distill-llama-8b.md) here, though they rank higher overall. Trails [EXAONE 3.5 2.4B Instruct](exaone-3-5-2-4b.md), [Code Llama 7B Instruct](codellama-7b-instruct.md), [Qwen2.5 1.5B Instruct](qwen2-5-1-5b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
HumanEval (maker) 61.6% ●·▄···█··█·▄ beats 0/6 easy · avg 84% 27% sharp · at its level
HumanEval+ (maker) 57.3% ●·▄····▄▄·▄█ beats 0/6 easy · avg 80% 20% sharp · at its level
Aider code editing (133 Exercism … 14.3% ●·▁▂▄▁▆▄█▆▂▂ beats 0/62 medium · avg 51% 17% sharp
MBPP (maker) 52.4% ●···███·██·█ beats 0/6 easy · avg 77% 12% at its level
MultiPL-E (maker) 49.6% ●·▄····▄▄·▄█ beats 0/6 easy · avg 70% 8.3% at its level
MBPP+ (maker) 43.7% ●····▄▄·▄·█▄ beats 0/6 easy · avg 66% 7.1% at its level
CRUXEval-I (maker) 33.9% ●·██·█··█·██ beats 0/6 medium · avg 56% 3.1% at its level
CRUXEval-O (maker) 27.8% ●·█··▄·▄··▄▄ beats 0/6 medium · avg 54% 1.1% blunt · at its level
other tests (1)
test result vs the others in its run difficulty counts why
BigCodeBench (Instruct) (maker) 11.1% ●······▄▄█·█ beats 0/6 medium · avg 37% 3.2% typical
source results share of the score
Qwen (submitted / maker-reported) 8 82% ▇▇▇▇▇▇▇▇▇▇
Aider code editing benchmark runs 1 17% ▇▇
prior: pull toward the middle 1% ▇
weighted average = 38.4
Licences: Aider code editing benchmark runs ([Apache-2.0](../../licenses/aider-polyglot-runs-Apache-2.0.txt))
## 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 Coder 0.5B Instruct (Q4_K_M, needs ~0.49 GB, from [huggingface.co/Qwen](https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B-Instruct-GGUF/tree/ebb2015119c907b064c512bf053e945850b5875f))
```sh
llama cli -hf Qwen/Qwen2.5-Coder-0.5B-Instruct-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf Qwen/Qwen2.5-Coder-0.5B-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 Coder 0.5B Instruct (Q4_K_M, needs ~0.49 GB, from [huggingface.co/Qwen](https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B-Instruct-GGUF/tree/ebb2015119c907b064c512bf053e945850b5875f))
```sh
lms get https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B-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 (~0.3 GB, Mac only) `lms get https://huggingface.co/lmstudio-community/Qwen2.5-Coder-0.5B-Instruct-MLX-4bit -y` · 8bit (~0.5 GB, Mac only) `lms get https://huggingface.co/lmstudio-community/Qwen2.5-Coder-0.5B-Instruct-MLX-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 Qwen2.5 Coder 0.5B Instruct (Q4_K_M, needs ~0.398 GB, from [ollama.com](https://ollama.com/library/qwen2.5-coder/tags))
```sh
ollama run qwen2.5-coder:0.5b
```
### 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 Qwen2.5 Coder 0.5B Instruct (4bit, needs ~0.3 GB, Mac only, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Qwen2.5-Coder-0.5B-Instruct-MLX-4bit/tree/d557912d8b701b9979611b8152de310a2ecc4671))
```sh
mlx_lm.chat --model lmstudio-community/Qwen2.5-Coder-0.5B-Instruct-MLX-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model lmstudio-community/Qwen2.5-Coder-0.5B-Instruct-MLX-4bit
```
Other versions: 8bit (~0.5 GB, Mac only) `mlx_lm.chat --model lmstudio-community/Qwen2.5-Coder-0.5B-Instruct-MLX-8bit`
Model repository and documentation: [huggingface.co/Qwen](https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B-Instruct)
[interactive version](https://whatmodel.sh/model/qwen2-5-coder-0-5b) · [JSON](https://whatmodel.sh/api?format=json&all=1)