[whatmodel.sh](../../README.md) / Qwen2 7B Instruct
# Qwen2 7B Instruct
Alibaba · released 2024-06 · 7.62B params · apache-2.0
**score 45.5** ±0.6 · #462 of 732 · from 22 benchmark results across 10 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Qwen2 7B Instruct among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁●▆█▄▅▅▄▄▂▁ #292 of 313* 0.9% 1
coding ▁▁▁▃▅●█▇▅▄▃▁ #352 of 469* 11% 1
chat & writing ▁▁▁▃▆▄●█▆▇▇▃ #282 of 402* 2.8% 1
math ▁▁▃●█▆▄█▄▂▁▁ #356 of 431 28% 5
agent tasks · #292 of 313 · 0.9% of its score
Beats [Bielik 11B v2.3 Instruct](bielik-11b-v2-3-instruct.md), [GLM-4 9B Chat](glm-4-9b-chat.md), [Command R (08-2024)](command-r-08-2024.md) here, though they rank higher overall. Trails [Granite 4.0 H 350M](granite-4-0-h-350m.md), [Qwen3 0.6B](qwen3-0-6b.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
BFCL v3 41.3% ▁▁▁▁▁●▅█▅▄▁▁ beats 17/114 medium · avg 51% 0.9% blunt · at its level
coding · #352 of 469 · 11% 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 [Phi-3-mini-128k-instruct (Apr 2024)](phi-3-mini-128k-instruct.md), [DeepSeek LLM 67B Chat](deepseek-llm-67b-chat.md), [OpenChat 3.5](openchat-3-5.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
CRUXEval (ZeroEval, 800 problems) 37.9% ▁▂●▃▆█▆▃▂·▁▁ beats 4/47 medium · avg 57% 11% sharp · at its level
chat & writing · #282 of 402 · 2.8% of its score
Beats [Ministral 3 14B Reasoning 2512](ministral-3-14b-reasoning.md), [Ministral 3 14B](ministral-3-14b.md), [Ministral 3 8B](ministral-3-8b.md) here, though they rank higher overall. Trails [EXAONE 3.5 2.4B Instruct](exaone-3-5-2-4b.md), [Flan-T5 XXL](flan-t5-xxl.md), [LFM2 1.2B](lfm2-1-2b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Arabic IFEval prompt-level (Incep… 38.1% ▁·▁▁▂▁▄▄▆●█▄ beats 22/38 medium · avg 46% 2.8% sharp · at its level
math · #356 of 431 · 28% of its score
Beats [GPT-3.5 Turbo (0125)](gpt-3-5-turbo-0125.md), [DBRX Instruct](dbrx-instruct.md), [Command R Plus (04-2024)](command-r-plus-04-2024.md) here, though they rank higher overall. Trails [OPT-2.7B](opt-2-7b.md), [Gemma 2B (base)](gemma-2b-base.md), [Stable Code 3B](stable-code-3b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
GSM8K (maker) 85.7% ▄···█●▄·▄·▄█ beats 3/8 very easy · avg 92% 10% sharp · several runs
GSM8K (ZeroEval, zero-shot) 80.1% ▁▄·▁▄●▅▅▅███ beats 10/43 very easy · avg 87% 7.1% sharp
MATH (subset unspecified) (maker) 52.9% ▃····●·▅·▅·█ beats 1/8 easy · avg 74% 4.6% at its level · several runs
Minerva MATH (maker) 19.5% ▃·····●··█▅▅ beats 1/8 hard · avg 34% 3.3% typical
MATH Level 5 (ZeroEval, 721 probl… 23.9% ▁▁▁▆●▂▇▇▄··▂ beats 12/38 hard · avg 30% 3.2% sharp
other tests (12)
test result vs the others in its run difficulty counts why
MMLU-Redux (ZeroEval, 2,778 quest… 66.9% ▁·▁▁▄●▄▅█▄▂▁ beats 10/53 easy · avg 75% 19% at its level
LiveBench 2024-07-26 26.6% ▁▁▁▂▂▃●█▆▃▅▃ beats 19/61 medium · avg 38% 14% at its level
BigCodeBench (Complete) (maker) 40.7% ▂▁▁▄▂▅●█▆▂▂▂ beats 29/58 medium · avg 48% 4.3% at its level
BigCodeBench (Instruct) (maker) 28.0% ▃▂·●▆██▆▅▃▃▃ beats 3/33 medium · avg 39% 4.2% at its level
BIG-Bench Hard 65.0% ▁▁▁▁▂▄▅▆●▃▃▁ beats 96/155 medium · avg 72% 3.0% sharp · at its level · small run
ZebraLogic (ZeroEval, 1,000 puzzl… 8.4% ▁▁●▆██▁▁·▁▁▁ beats 5/57 hard · avg 15% 2.9% sharp
MMLU 70.8% ▁▁▁▁▁▂▂▅█●▆▄ beats 141/214 easy · avg 77% 2.8% at its level
Polish EQ-Bench v2 (SpeakLeash) 53.7% ▁··▁▁▂▂▆●▆█▆ beats 28/70 medium · avg 62% 2.4% at its level
Open Persian LLM Leaderboard v2 a… 50.0% ▂▄▂▃●▄▃▆▃▅▂▂ beats 28/81 medium · avg 57% 1.8% blunt · at its level
GPQA (subset unspecified) 38.4% ▂▁▂▂▄▃●▃▅█▃▃ beats 34/106 hard · avg 38% 1.6% sharp
Open PT-LLM Leaderboard average (… 72.1% ▃▁▁▁▁▁▁▁▂●▅▁ beats 192/276 medium · avg 72% 0.9% at its level · noisy
CMMLU 80.9% ▅·▅····█●▅█▅ beats 7/14 medium · avg 73% 0.7% at its level · noisy
source results share of the score
WildEval ZeroEval published results 5 43% ▇▇▇▇▇
InternLM GitHub repository model cards 6 15% ▇▇
LiveBench model judgments 1 14% ▇▇
Qwen (submitted / maker-reported) 3 11% ▇
BigCodeBench paper (submitted / maker-r… 2 8% ▇
Inception Arabic/Hindi leaderboards 1 3% ▇
SpeakLeash Polish EQ-Bench leaderboard 1 2% ▇
Open Persian LLM Leaderboard v2 1 2% ▇
Open Portuguese LLM Leaderboard 1 1% ▇
Berkeley Function Calling Leaderboard r… 1 1% ▇
prior: pull toward the middle 0% ▇
weighted average = 45.5
Licences: WildEval ZeroEval published results ([Apache-2.0](../../licenses/zeroeval-published-results-Apache-2.0.txt)) · InternLM GitHub repository model cards ([Apache-2.0](https://github.com/InternLM/InternLM/blob/68fdc71330059255fd647abf28219ad11e8e4865/LICENSE)) · LiveBench model judgments ([Apache-2.0 (stated in the LiveBench datasheet)](https://www.apache.org/licenses/LICENSE-2.0)) · BigCodeBench paper ([CC BY 4.0](http://creativecommons.org/licenses/by/4.0/)) · Inception Arabic/Hindi leaderboards ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · SpeakLeash Polish EQ-Bench leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · Open Persian LLM Leaderboard v2 ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · Berkeley Function Calling Leaderboard result archive ([Apache-2.0](../../licenses/bfcl-result-archive-v4-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 7B Instruct (Q4_K_M, needs ~4.7 GB, from [huggingface.co/Qwen](https://huggingface.co/Qwen/Qwen2-7B-Instruct-GGUF/tree/c3024c6fff0a02d52119ecee024bbb93d4b4b8e4))
```sh
llama cli -hf Qwen/Qwen2-7B-Instruct-GGUF:q4_k_m
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf Qwen/Qwen2-7B-Instruct-GGUF:q4_k_m
```
Other versions: Q8_0 (~8.1 GB) `llama cli -hf Qwen/Qwen2-7B-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 7B Instruct (Q4_K_M, needs ~4.7 GB, from [huggingface.co/Qwen](https://huggingface.co/Qwen/Qwen2-7B-Instruct-GGUF/tree/c3024c6fff0a02d52119ecee024bbb93d4b4b8e4))
```sh
lms get https://huggingface.co/Qwen/Qwen2-7B-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/Qwen/Qwen2-7B-Instruct-GGUF@q8_0 -y` · 4bit (~4.3 GB, Mac only) `lms get https://huggingface.co/mlx-community/Qwen2-7B-Instruct-4bit -y` · 8bit (~8.1 GB, Mac only) `lms get https://huggingface.co/mlx-community/Qwen2-7B-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 7B Instruct (Q4_K_M, needs ~5.07 GB, from [ollama.com](https://ollama.com/library/qwen2/tags))
```sh
ollama run qwen2:7b-instruct-q4_K_M
```
Other versions: Q8_0 (~9.64 GB) `ollama run qwen2:7b-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 7B Instruct (4bit, needs ~4.3 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/Qwen2-7B-Instruct-4bit/tree/daf4e06b242919852061074afddf8133e53e1d66))
```sh
mlx_lm.chat --model mlx-community/Qwen2-7B-Instruct-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/Qwen2-7B-Instruct-4bit
```
Other versions: 8bit (~8.1 GB, Mac only) `mlx_lm.chat --model mlx-community/Qwen2-7B-Instruct-8bit`
Model repository and documentation: [huggingface.co/Qwen](https://huggingface.co/Qwen/Qwen2-7B-Instruct)
[interactive version](https://whatmodel.sh/model/qwen2-7b) · [JSON](https://whatmodel.sh/api?format=json&all=1)