[whatmodel.sh](../../README.md) / Qwen1.5 72B Chat
# Qwen1.5 72B Chat
Alibaba · released 2024-02 · 72.29B params · tongyi-qianwen
**score 46.9** ±0.5 · #439 of 732 · from 19 benchmark results across 15 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Qwen1.5 72B Chat among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃▅●█▇▅▄▃▁ #328 of 469 44% 3
chat & writing ▁▁▁▃▆●▆█▆▇▇▃ #295 of 402 21% 5
coding · #328 of 469 · 44% of its score
Beats [Qwen3-VL 8B Instruct](qwen3-vl-8b-instruct.md), [Qwen3-VL 4B Instruct](qwen3-vl-4b-instruct.md), [LFM2.5 2.6B](lfm2-5-2-6b.md) here, though they rank higher overall. Trails [DeepSeek LLM 67B Chat](deepseek-llm-67b-chat.md), [WizardLM 70B V1.0](wizardlm-70b-v1-0.md), [Llama 3.2 11B Vision Instruct](llama-3-2-11b-vision.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
HumanEval 68.3% ▁·▁▁▃▄▆●█▃▁▁ beats 54/99 easy · avg 76% 27% sharp · at its level · several runs
HumanEval+ 59.1% ▁·▁▁▁▃▄▄●█▃▁ beats 50/99 easy · avg 70% 14% sharp · at its level
MBPP+ 61.6% ▁···▁▁▂▅▇●▆▂ beats 40/75 easy · avg 66% 2.8% at its level
chat & writing · #295 of 402 · 21% of its score
Beats [Ministral 3 14B Reasoning 2512](ministral-3-14b-reasoning.md), [Gemma 4 E4B](gemma-4-e4b.md), [OpenThinker3 7B](openthinker3-7b.md) here, though they rank higher overall. Trails [EXAONE 3.5 2.4B Instruct](exaone-3-5-2-4b.md), [LFM2 700M](lfm2-700m.md), [Flan-T5 XXL](flan-t5-xxl.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
AlpacaEval 2 length-controlled 36.6% ▁▂▂▃▆▆█●▄▄▁▁ beats 92/122 medium · avg 35% 8.8% sharp · at its level
Arena-Hard (v0.1, GPT-4-1106 judg… 36.1% ▂▁▁▅█●▄▃▃▅▃▁ beats 30/60 medium · avg 47% 5.6% sharp · at its level
LMArena (text) 1233.6 ▁▁▁▂▃●▃▆▆▇█▃ beats 84/385 avg 1260 2.5% blunt
Polyrating rating (bias-aware re-… 1148.0 ▂▁▂▁▃▇█▆●▄▂▂ beats 65/95 avg 1187 2.3% typical
Arabic IFEval prompt-level (Incep… 14.1% ▁·▁▁▂▁●▄▆▄█▄ beats 8/38 medium · avg 46% 1.9% sharp · at its level
other tests (10)
test result vs the others in its run difficulty counts why
LiveBench 2024-07-26 29.3% ▁▁▁▂▂▃▂●▆▃▅▃ beats 25/61 medium · avg 38% 9.3% at its level
EvoEval (overall) 42.0% ▂▂▃▄▄█▁●▅▃▁▃ beats 28/42 medium · avg 52% 7.1% at its level
MMLU-Pro 47.1% ▃▂▅█●·█▃▅··▂ beats 11/23 medium · avg 54% 4.1% at its level
BigCodeBench (Instruct) (maker) 32.6% ▃▂·▆▆●█▆▅▃▃▃ beats 11/33 medium · avg 39% 2.8% at its level
BigCodeBench (Complete) (maker) 39.8% ▂▁▁▄▂▅●█▆▂▂▂ beats 27/58 medium · avg 48% 2.8% at its level
WildBench v2 WB-Score (task macro) 39.9277 WB-Score ▁·▁▁·▃█▄●▆▅▆ beats 30/52 avg 43.019 WB-Score 2.7% sharp
CPTUB average (SpeakLeash, 0-5 ju… 63.2% ▁▂▂▂▃●▃▆▅▄▂▃ beats 42/95 medium · avg 65% 2.0% at its level
GPQA Diamond 28.8% ▁▁▁●▂▄▆▄▅▇█▅ beats 15/211 hard · avg 39% 1.8% sharp
Polish EQ-Bench v2 (SpeakLeash) 68.0% ▁··▁▁▂▂▆▅▆●▆ beats 47/70 medium · avg 62% 1.6% at its level
Open PT-LLM Leaderboard average (… 74.3% ▃▁▁▁▁▁▁▁▂█●▁ beats 217/276 medium · avg 72% 0.6% at its level · noisy
source results share of the score
EvalPlus / EvalPerf published evaluatio… 3 34% ▇▇▇▇
Top Leaderboard Ranking = Top Coding Pr… 2 17% ▇▇
LiveBench model judgments 1 9% ▇
AlpacaEval 1.0 and 2.0 leaderboards 1 9% ▇
Arena-Hard-Auto v0.1 leaderboard 1 6% ▇
BigCodeBench paper (submitted / maker-r… 2 6% ▇
MMLU-Pro author-released evaluation runs 1 4% ▇
WildBench v2 leaderboard data 1 3% ▇
LMArena leaderboard dataset 1 3% ▇
Polyrating: A Cost-Effective and Bias-A… 1 2% ▇
SpeakLeash CPTUB 1 2% ▇
Inception Arabic/Hindi leaderboards 1 2% ▇
Epoch AI 1 2% ▇
SpeakLeash Polish EQ-Bench leaderboard 1 2% ▇
Open Portuguese LLM Leaderboard 1 1% ▇
prior: pull toward the middle 0% ▇
weighted average = 46.9
Licences: EvalPlus / EvalPerf published evaluation runs ([Apache-2.0](../../licenses/evalplus-published-runs-Apache-2.0.txt)) · Top Leaderboard Ranking = Top Coding Proficiency ([CC BY 4.0](../../licenses/paper-2403.19114v1-CC-BY-4.0.txt)) · LiveBench model judgments ([Apache-2.0 (stated in the LiveBench datasheet)](https://www.apache.org/licenses/LICENSE-2.0)) · AlpacaEval 1.0 and 2.0 leaderboards ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · Arena-Hard-Auto v0.1 leaderboard ([Apache-2.0](../../licenses/arena-hard-auto-v0-1-Apache-2.0.txt)) · BigCodeBench paper ([CC BY 4.0](http://creativecommons.org/licenses/by/4.0/)) · MMLU-Pro author-released evaluation runs ([Apache-2.0](../../licenses/mmlu-pro-author-runs-Apache-2.0.txt)) · WildBench v2 leaderboard data ([Apache-2.0](https://github.com/allenai/WildBench/blob/d6b8dcaf377d173d031980f97c16e1a82618c03d/LICENSE)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Polyrating: A Cost-Effective and Bias-Aware Rating System for LLM Evaluation ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · SpeakLeash CPTUB ([MIT](../../licenses/hf-leaderboard-spaces-MIT.txt)) · Inception Arabic/Hindi leaderboards ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · Epoch AI ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · SpeakLeash Polish EQ-Bench leaderboard ([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))
## 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 Qwen1.5 72B Chat (Q4_K_M, needs ~44.1 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/Qwen1.5-72B-Chat-GGUF/tree/5dd6f93c713ebcf1ca0c2e0cebd5183b83e1e4c6))
```sh
llama cli -hf mradermacher/Qwen1.5-72B-Chat-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf mradermacher/Qwen1.5-72B-Chat-GGUF:Q4_K_M
```
Other versions: Q2_K (~28.5 GB) `llama cli -hf mradermacher/Qwen1.5-72B-Chat-GGUF:Q2_K`
### 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 Qwen1.5 72B Chat (Q4_K_M, needs ~44.1 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/Qwen1.5-72B-Chat-GGUF/tree/5dd6f93c713ebcf1ca0c2e0cebd5183b83e1e4c6))
```sh
lms get https://huggingface.co/mradermacher/Qwen1.5-72B-Chat-GGUF@Q4_K_M -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
Other versions: Q2_K (~28.5 GB) `lms get https://huggingface.co/mradermacher/Qwen1.5-72B-Chat-GGUF@Q2_K -y` · 4bit (~42.5 GB, Mac only) `lms get https://huggingface.co/mlx-community/Qwen1.5-72B-Chat-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 Qwen1.5 72B Chat (Q4_0, needs ~43.87 GB, from [ollama.com](https://ollama.com/library/qwen/tags))
```sh
ollama run qwen:72b-chat
```
Other versions: Q8_0 (~87.25 GB) `ollama run qwen:72b-chat-v1.5-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 Qwen1.5 72B Chat (4bit, needs ~42.5 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/Qwen1.5-72B-Chat-4bit/tree/99d27742360d00b919ec1f662f3b1057e4a4dba3))
```sh
mlx_lm.chat --model mlx-community/Qwen1.5-72B-Chat-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/Qwen1.5-72B-Chat-4bit
```
Model repository and documentation: [huggingface.co/Qwen](https://huggingface.co/Qwen/Qwen1.5-72B-Chat)
[interactive version](https://whatmodel.sh/model/qwen1-5-72b-chat) · [JSON](https://whatmodel.sh/api?format=json&all=1)