[whatmodel.sh](../../README.md) / Qwen2.5 Coder 1.5B Instruct
# Qwen2.5 Coder 1.5B Instruct
Alibaba · released 2024-09 · 1.54B params · apache-2.0
**score 44.0** ±1.1 · #502 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 1.5B Instruct among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃▅●█▇▅▄▃▁ #356 of 469 96% 8
coding · #356 of 469 · 96% of its score
Beats [LFM2.5 2.6B](lfm2-5-2-6b.md), [LFM2.5 8B-A1B](lfm2-5-8b-a1b.md), [Qwen3-VL 2B Thinking](qwen3-vl-2b-thinking.md) here, though they rank higher overall. Trails [Code Tulu 2 13B](code-tulu-2-13b.md), [Phi-3-mini-128k-instruct (Apr 2024)](phi-3-mini-128k-instruct.md), [DeepSeek LLM 67B Chat](deepseek-llm-67b-chat.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
HumanEval (maker) 70.7% ▄·●···█··█·▄ beats 1/6 easy · avg 84% 24% sharp · at its level
Aider code editing (133 Exercism … 31.6% ▁·▁●▄▁▆▄█▆▂▂ beats 3/62 medium · avg 51% 23% sharp · at its level
HumanEval+ (maker) 66.5% ▄·●····▄▄·▄█ beats 1/6 easy · avg 80% 19% sharp · at its level
MBPP (maker) 69.2% █···●██·██·█ beats 1/6 easy · avg 77% 11% at its level
MultiPL-E (maker) 56.7% ▄·●····▄▄·▄█ beats 1/6 easy · avg 70% 8.1% at its level
MBPP+ (maker) 59.4% ▄····●▄·▄·█▄ beats 1/6 easy · avg 66% 7.0% at its level
CRUXEval-I (maker) 45.4% █·█●·█··█·██ beats 2/6 medium · avg 56% 3.3% at its level
CRUXEval-O (maker) 37.5% ▄·●··▄·▄··▄▄ beats 1/6 medium · avg 54% 1.2% blunt · at its level
other tests (1)
test result vs the others in its run difficulty counts why
BigCodeBench (Instruct) (maker) 32.5% ▄······●▄█·█ beats 1/6 medium · avg 37% 3.8% at its level
source results share of the score
Qwen (submitted / maker-reported) 8 77% ▇▇▇▇▇▇▇▇▇
Aider code editing benchmark runs 1 23% ▇▇▇
prior: pull toward the middle 1% ▇
weighted average = 44.0
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 1.5B Instruct (Q4_K_M, needs ~1.1 GB, from [huggingface.co/Qwen](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF/tree/f86cb2c1fa58255f8052cc32aeede1b7482d4361))
```sh
llama cli -hf Qwen/Qwen2.5-Coder-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 Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF:q4_k_m
```
Other versions: Q8_0 (~1.9 GB) `llama cli -hf Qwen/Qwen2.5-Coder-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 Qwen2.5 Coder 1.5B Instruct (Q4_K_M, needs ~1.1 GB, from [huggingface.co/Qwen](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF/tree/f86cb2c1fa58255f8052cc32aeede1b7482d4361))
```sh
lms get https://huggingface.co/Qwen/Qwen2.5-Coder-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 (~1.9 GB) `lms get https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF@q8_0 -y` · 4bit (~0.9 GB, Mac only) `lms get https://huggingface.co/lmstudio-community/Qwen2.5-Coder-1.5B-Instruct-MLX-4bit -y` · 8bit (~1.6 GB, Mac only) `lms get https://huggingface.co/lmstudio-community/Qwen2.5-Coder-1.5B-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 1.5B Instruct (Q4_K_M, needs ~0.99 GB, from [ollama.com](https://ollama.com/library/qwen2.5-coder/tags))
```sh
ollama run qwen2.5-coder:1.5b
```
Other versions: Q8_0 (~1.6 GB) `ollama run qwen2.5-coder:1.5b-instruct-q8_0`
### 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 1.5B Instruct (4bit, needs ~0.9 GB, Mac only, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Qwen2.5-Coder-1.5B-Instruct-MLX-4bit/tree/5eb71775079ff4a1faa4d80babb71681376ea720))
```sh
mlx_lm.chat --model lmstudio-community/Qwen2.5-Coder-1.5B-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-1.5B-Instruct-MLX-4bit
```
Other versions: 8bit (~1.6 GB, Mac only) `mlx_lm.chat --model lmstudio-community/Qwen2.5-Coder-1.5B-Instruct-MLX-8bit`
Model repository and documentation: [huggingface.co/Qwen](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct)
[interactive version](https://whatmodel.sh/model/qwen2-5-coder-1-5b) · [JSON](https://whatmodel.sh/api?format=json&all=1)