[whatmodel.sh](../../README.md) / Qwen2.5 Coder 7B Instruct
# Qwen2.5 Coder 7B Instruct
Alibaba · released 2024-09 · 7.62B params · apache-2.0
**score 52.5** ±1.0 · #328 of 732 · from 15 benchmark results across 5 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 7B Instruct among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃▅▇●▇▅▄▃▁ #253 of 469 94% 13
coding · #253 of 469 · 94% of its score
Beats [Qwen3-VL 32B Instruct](qwen3-vl-32b-instruct.md), [Solar Open 100B (102B-A12B)](solar-open-100b.md), [GLM-4.7-Flash](glm-4-7-flash.md) here, though they rank higher overall. Trails [QwQ-32B-Preview](qwq-32b-preview.md), [InternLM2.5 20B Chat](internlm2-5-20b-chat.md), [Ministral 3 3B Reasoning 2512](ministral-3-3b-reasoning.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
LiveCodeBench official (Jan 1–Nov… 26.2% ▁▁·▁▂▄●█▅▂·▂ beats 13/31 hard · avg 23% 22% sharp · at its level
Aider code editing (133 Exercism … 57.9% ▁·▁▂▄▁▆●█▆▂▂ beats 26/62 medium · avg 51% 20% sharp · at its level
HumanEval (maker) 88.4% ▄·▄···█··●·▄ beats 4/6 easy · avg 84% 11% sharp
HumanEval+ (maker) 84.1% ▄·▄····▄▄·●█ beats 4/6 easy · avg 80% 10% sharp
MBPP (maker) 83.5% █···███·●█·█ beats 4/6 easy · avg 77% 6.5% typical
MultiPL-E (maker) 76.5% ▄·▄····▄▄·●█ beats 4/6 easy · avg 70% 5.5% at its level
MBPP+ (maker) 71.7% ▄····▄▄·▄·●▄ beats 4/6 easy · avg 66% 5.0% at its level
CodeReviewQA: change localisation… 10.7% █▂▁▁▂▂●▃▅▅▅▇ beats 17/47 hard · avg 33% 4.0% sharp · at its level · noisy
CodeReviewQA: solution identifica… 55.2% ▃··▁▂▂▃▂●▃▄▂ beats 30/47 medium · avg 51% 3.2% sharp · at its level · noisy
CodeReviewQA: automated code refi… 41.0% ▁▁▁▁▁▁▂▂▃█●▄ beats 40/47 hard · avg 32% 2.9% at its level
CRUXEval-I (maker) 65.8% █·██·█··●·██ beats 4/6 medium · avg 56% 2.6% at its level
CRUXEval-O (maker) 65.9% ▄·█··▄·●··▄▄ beats 4/6 medium · avg 54% 0.9% blunt · at its level
CodeReviewQA: change type recogni… 78.6% ▁▁▁······▁▃● beats 43/47 easy · avg 67% 0.1% blunt · at its level · noisy
other tests (2)
test result vs the others in its run difficulty counts why
BigCodeBench (Instruct) (maker) 41.0% ▄······▄▄●·█ beats 4/6 medium · avg 37% 3.5% at its level
Open PT-LLM Leaderboard average (… 71.7% ▃▁▁▁▁▁▁▁▂●▅▁ beats 187/276 medium · avg 72% 2.4% at its level · noisy
source results share of the score
Qwen (submitted / maker-reported) 8 45% ▇▇▇▇▇
LiveCodeBench official website results 1 22% ▇▇▇
Aider code editing benchmark runs 1 20% ▇▇
CodeReviewQA: The Code Review Comprehen… 4 10% ▇
Open Portuguese LLM Leaderboard 1 2% ▇
prior: pull toward the middle 0% ▇
weighted average = 52.5
Licences: LiveCodeBench official website results ([CC-BY-SA-4.0](../../licenses/lcb-sharealike-NOTICE.txt)) · Aider code editing benchmark runs ([Apache-2.0](../../licenses/aider-polyglot-runs-Apache-2.0.txt)) · CodeReviewQA: The Code Review Comprehension Assessment for Large Language Models ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-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 7B Instruct (Q4_K_M, needs ~4.7 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Qwen2.5-Coder-7B-Instruct-GGUF/tree/ba62cce5619ff5abbe5eceefab7caa4b11c267bd))
```sh
llama cli -hf lmstudio-community/Qwen2.5-Coder-7B-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/Qwen2.5-Coder-7B-Instruct-GGUF:Q4_K_M
```
Other versions: Q8_0 (~8.1 GB) `llama cli -hf lmstudio-community/Qwen2.5-Coder-7B-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 7B Instruct (Q4_K_M, needs ~4.7 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Qwen2.5-Coder-7B-Instruct-GGUF/tree/ba62cce5619ff5abbe5eceefab7caa4b11c267bd))
```sh
lms get https://huggingface.co/lmstudio-community/Qwen2.5-Coder-7B-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 (~8.1 GB) `lms get https://huggingface.co/lmstudio-community/Qwen2.5-Coder-7B-Instruct-GGUF@Q8_0 -y` · 4bit (~4.3 GB, Mac only) `lms get https://huggingface.co/lmstudio-community/Qwen2.5-Coder-7B-Instruct-MLX-4bit -y` · 8bit (~8.1 GB, Mac only) `lms get https://huggingface.co/lmstudio-community/Qwen2.5-Coder-7B-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 7B Instruct (Q4_K_M, needs ~5.07 GB, from [ollama.com](https://ollama.com/library/qwen2.5-coder/tags))
```sh
ollama run qwen2.5-coder:7b
```
Other versions: Q8_0 (~9.64 GB) `ollama run qwen2.5-coder:7b-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 7B Instruct (4bit, needs ~4.3 GB, Mac only, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Qwen2.5-Coder-7B-Instruct-MLX-4bit/tree/84d7d10913033a123d8bb47c065368d62b5041cb))
```sh
mlx_lm.chat --model lmstudio-community/Qwen2.5-Coder-7B-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-7B-Instruct-MLX-4bit
```
Other versions: 8bit (~8.1 GB, Mac only) `mlx_lm.chat --model lmstudio-community/Qwen2.5-Coder-7B-Instruct-MLX-8bit`
Model repository and documentation: [huggingface.co/Qwen](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct)
## Published speed measurements
Reference results for the hardware, engine and settings below. These are not verified speeds of the downloads above or predictions for your machine.
hardware precision speed engine and settings
Intel 3rd Gen Core processo… q4_k_m 2.26 tok/s ollama (version not recorded) · l…
Intel CoffeeLake-S GT2 [UHD… q4_k_m 3.78 tok/s ollama (version not recorded) · l…
RTX 4060 Laptop q4_k_m 47.73 tok/s ollama (version not recorded) · l…
Each speed links to its source, which has the full settings.
## Quantization evidence
How much of the full-precision result a quantized version kept, measured within one study. Rows from the same study share a table and aren't independent. They don't describe every download of that precision.
precision test full → quantized kept
AQLM 4-bit BigCodeBench (Python) pass@1 0.41 → 0.41 100.0%
AWQ 4-bit BigCodeBench (Python) pass@1 0.41 → 0.41 100.0%
bitsandbytes NF4 (double quan… BigCodeBench (Python) pass@1 0.41 → 0.39 95.1%
GGUF Q4_K_M BigCodeBench (Python) pass@1 0.41 → 0.4 97.6%
GPTQ 4-bit BigCodeBench (Python) pass@1 0.41 → 0.41 100.0%
QuIP# 4-bit BigCodeBench (Python) pass@1 0.41 → 0.32 78.0%
Each kept value links to its source.
[interactive version](https://whatmodel.sh/model/qwen2-5-coder-7b) · [JSON](https://whatmodel.sh/api?format=json&all=1)