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Qwen-7B-Chat

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Qwen-7B-Chat is an open-weights language model from Alibaba, released August 2023. Its published size is 7.72B parameters and a 33k-token context window. Licence: other (tongyi-qianwen-license-agreement).

Catalogue identity notes

Maker repo Qwen/Qwen-7B-Chat @93a65d3: licence tag other (tongyi-qianwen-license-agreement). 7,721,324,544 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2023-08: month the maker's Hugging Face repo was created (createdAt 2023-08-03).

30.9±0.9#635 of 719 LLMsfrom 11 benchmark results across 2 sources

$ how it compares

The LLMs ranked either side of Qwen-7B-Chat, out of 828 with a score, then more from Alibaba.

#modelscorewhereRAMreleased
730Flan-T5 XL Google31±2local~14GB2022-10
731Qwen2 1.5B Instruct Alibaba31±1local~2.4GB2024-06
732Qwen-7B-Chat Alibaba31±1local~6.6GB2023-08
733Gemma 3 1B Google31±1local~2.2GB2025-03
734StarCoderBase 7B BigCode31±1local~6GB2023-07
more from Alibaba
18Qwen3.8 Max (2.4T-A95B) Alibaba78±1api $3.00/M +local~380GB2026-08
34Qwen3.8 Max (0902) Alibaba76±1api $3.00/M2026-09
37Qwen3.8-Flash-Next Alibaba75±1local~59GB2026-08

see all LLMs · find the ones that fit your machine

$ what it's good at

other tests11 not in a task · 100% of its score
testresultvs the others in its rundifficultycountswhy
ARC Challenge46.7% ✎▁·▁··▁▁▇▂█▆▃ beats 22/26easy · avg 85%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 49%sharp · at its level
WinoGrande68.6% ✎▁····▁··▁▂█▄ beats 20/26easy · avg 89%▇▇▇▇▇ 12%at its level
BoolQ71.7% ✎▂▃▂▅▆▂▅▆█▂▂▂ beats 16/26easy · avg 91%▇▇▇▇ 9.2%at its level
ARC Easy64.5% ✎▁▁·▁▁▄▅▄█▅▁▃ beats 12/26easy · avg 88%▇▇▇ 6.4%at its level
HellaSwag85.0% ✎▃···▁▇▅█▇▃▃▁ beats 25/26very easy · avg 92%▇▇ 5.1%at its level
SciQ90.7% ✎▁·▁·▁·▃▅▃█▄▁ beats 9/26very easy · avg 98%▇▇ 4.6%typical
PIQA78.7% ✎▁·▁··▁▁▃█▅█▂ beats 20/26easy · avg 88%▇▇ 4.1%blunt · at its level
OpenBookQA35.6% ✎█·▁▁▃·▁▃▅▇▃▄ beats 25/26medium · avg 53%▇▇ 3.9%typical
3 more tests
testresultvs the others in its rundifficultycountswhy
Social IQA47.0% ✎▁···▁▃█▁▁▃·▁ beats 24/26medium · avg 59%▇ 2.5%blunt
LAMBADA (OpenAI version), last-word accuracy65.5% ✎▁··▂▁▁▁▃▆█▅▃ beats 7/26very easy · avg 89%▇ 2.4%blunt · at its level
Open PT-LLM Leaderboard average (9 Portuguese tasks)59.9% ✎▃▁▁▁▁▁▁▁▃█▅▁ beats 89/276medium · avg 73%▇ 1.2%noisy

$ where to run it

llama.cpp · recommended

version

  1. Install llama.cpp · Homebrew and winget installs name the commands llama-cli and llama-server instead.
    macOS / Linux
    curl -LsSf https://llama.app/install.sh | sh
    Windows
    irm https://llama.app/install.ps1 | iex

    other ways to install ↗

  2. Run Qwen-7B-Chat · needs ~4.9 GB · from huggingface.co/mradermacher
    chat in the terminal
    llama cli -hf mradermacher/Qwen-7B-Chat-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf mradermacher/Qwen-7B-Chat-GGUF:Q4_K_M

LM Studio

version

  1. Install LM Studio · Open the app once before using the lms command.

    download it (macOS / Windows / Linux) ↗

  2. Run Qwen-7B-Chat · needs ~4.9 GB · from huggingface.co/mradermacher

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/mradermacher/Qwen-7B-Chat-GGUF@Q4_K_M -y
  4. Then run lms ls to see its name, and lms chat followed by that name

Ollama

version

  1. Install Ollama

    download it (macOS / Windows) ↗

    Linux
    curl -fsSL https://ollama.com/install.sh | sh
  2. Run Qwen-7B-Chat · needs ~4.9 GB · from huggingface.co/mradermacher
    chat in the terminal
    ollama run hf.co/mradermacher/Qwen-7B-Chat-GGUF:Q4_K_M

Other ways to run it

  1. Run it as published · needs ~15.44 GB · from huggingface.co/Qwen (official)
    uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='Qwen/Qwen-7B-Chat', torch_dtype='auto', device_map='auto', trust_remote_code=True)('Once upon a time', max_new_tokens=40)[0]['generated_text'])"

Official weights and documentation: huggingface.co/Qwen ↗

$ how the score was worked out · Stability AI — original Stabl… 98%, Open Portuguese LLM Leaderboa… 1%

Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its counts column above; here it's added up by who ran the tests. How it works

sourceresultsshare of the scorelicence
Stability AI — original StableLM zero-shot evaluation outputs10▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 98%Apache-2.0
Open Portuguese LLM Leaderboard (archived)1▇ 1%Apache-2.0
prior · pull toward the middle▇ 0%
weighted average= 30.9

$ improve this page

Update model informationAdd a place to run itAdd a test resultPropose a benchmarkCorrect a benchmark scoreEdit data on GitHub

Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.

data updated · sources and licences · json