whatmodel.sh / calme-2.1-qwen2.5-72b

calme-2.1-qwen2.5-72b

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calme-2.1-qwen2.5-72b is an open-weights language model from MaziyarPanahi, released September 2024. Its published size is 72.7B parameters. Licence: other (tongyi-qianwen).

Catalogue identity notes

Maker repo MaziyarPanahi/calme-2.1-qwen2.5-72b @eb6c92d: licence tag other (tongyi-qianwen). 72,699,666,432 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2024-09: month the maker's Hugging Face repo was created (createdAt 2024-09-19).

57.9±4.3#254 of 949 LLMsfrom 1 benchmark result across 1 source* thinly tested, treat the rank as provisional

$ how it compares

The LLMs ranked either side of calme-2.1-qwen2.5-72b, out of 1132 with a score, then more from MaziyarPanahi.

#modelscorewhereRAMreleased
333Phi-4-reasoning 14B Microsoft58±1api $0.22/M +local~10GB2025-04
334Olmo 3 32B Think Ai258±1local~21GB2025-11
335calme-2.1-qwen2.5-72b MaziyarPanahi58±4local~33GB2024-09
336Magistral Small 1.2 Mistral58±1api $0.75/M +local~16GB2025-09
337Step 1o Turbo (2025-06) StepFun58±3comparison only
more from MaziyarPanahi
342calme-2.2-qwen2.5-72b MaziyarPanahi58±4local~33GB2024-09

see all LLMs · find the ones that fit your machine

$ what it's good at

other tests1 not in a task · 92% of its score
testresultvs the others in its rundifficultycountswhy
AraGen-v3 3C3H score (Inception, Claude Sonnet 4 judge)50.8% ✎▁▁·▁▂▂▄█▄▅▄▄ beats 46/66hard · avg 31%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 92%at its level

$ 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 calme-2.1-qwen2.5-72b · needs ~47.4 GB · from huggingface.co/mradermacher
    chat in the terminal
    llama cli -hf mradermacher/calme-2.1-qwen2.5-72b-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf mradermacher/calme-2.1-qwen2.5-72b-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 calme-2.1-qwen2.5-72b · needs ~47.4 GB · from huggingface.co/mradermacher

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/mradermacher/calme-2.1-qwen2.5-72b-GGUF@Q4_K_M -y
  4. Then run lms ls to see its name, and lms chat followed by that name

Ollama

  1. Install Ollama

    download it (macOS / Windows) ↗

    Linux
    curl -fsSL https://ollama.com/install.sh | sh
  2. Run calme-2.1-qwen2.5-72b · needs ~29.8 GB · from huggingface.co/mradermacher
    chat in the terminal
    ollama run hf.co/mradermacher/calme-2.1-qwen2.5-72b-GGUF:Q2_K

Other ways to run it

  1. Run it as published · needs ~145.4 GB · from huggingface.co/MaziyarPanahi (official)
    uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='MaziyarPanahi/calme-2.1-qwen2.5-72b', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"

Official weights and documentation: huggingface.co/MaziyarPanahi ↗

$ how the score was worked out · Inception Arabic/Hindi leader… 92%

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
Inception Arabic/Hindi leaderboards (AraGen-v3, HindiGen-v1, Arabic IFEval)1▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 92%Apache-2.0
prior · pull toward the middle▇▇▇ 8%
weighted average= 57.9

$ 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