whatmodel.sh / Command R7B Arabic (02-2025)

Command R7B Arabic (02-2025)

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Command R7B Arabic (02-2025) is an open-weights language model from Cohere, released February 2025. Its published size is 8.03B parameters. Licence: cc-by-nc-4.0.

Catalogue identity notes

CohereLabs/c4ai-command-r7b-arabic-02-2025 @2d5440c (created 2025-02-27, licence cc-by-nc-4.0). Cohere's Arabic-optimised Command R7B (Feb 2025; gated, CC BY-NC). Independent row: Inception AraGen-v3 (3C3H).

Model information sources

Release date ↗ · Weights licence ↗

HF safetensors metadata 8,028,033,024 parameters.

48.5±4.8#487 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 Command R7B Arabic (02-2025), out of 1132 with a score, then more from Cohere.

#modelscorewhereRAMreleased
621InternLM2.5 7B Chat Shanghai AI Laboratory (InternLM)49±2local~6.1GB2024-06
622FsfairX-Zephyr-Chat-v0.1 sfairXC (RLHFlow)49±2local~6.1GB2024-04
623Command R7B Arabic (02-2025) Cohere49±5local~6.9GB2025-02
624Hermes 3 Llama 3.1 70B Nous Research49±1local~44GB2024-07
625Qwen2.5 7B Instruct Alibaba48±1api $0.30/M +local~6GB2024-09
more from Cohere
85North Small Translate 1.0 Cohere71±6api price unknown2026-08
205Command A+ (05-2026) Cohere64±1api price unknown +local~71GB2026-05
357Command A (03-2025) Cohere57±1api $4.38/M +local~37GB2025-03

see all LLMs · find the ones that fit your machine

$ what it's good at

other tests1 not in a task · 90% of its score
testresultvs the others in its rundifficultycountswhy
AraGen-v3 3C3H score (Inception, Claude Sonnet 4 judge)27.9% ✎▁▁·▁▂▂▄█▄▅▄▄ beats 24/66hard · avg 31%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 90%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 Command R7B Arabic (02-2025) · needs ~5.1 GB · from huggingface.co/matrixportalx
    chat in the terminal
    llama cli -hf matrixportalx/c4ai-command-r7b-arabic-02-2025-GGUF:q4_k_m
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf matrixportalx/c4ai-command-r7b-arabic-02-2025-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 Command R7B Arabic (02-2025) · needs ~5.1 GB · from huggingface.co/matrixportalx

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/matrixportalx/c4ai-command-r7b-arabic-02-2025-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 Command R7B Arabic (02-2025) · needs ~5.1 GB · from ollama.com
    chat in the terminal
    ollama run command-r7b-arabic:7b

Weights and documentation: huggingface.co/CohereLabs ↗

$ how the score was worked out · Inception Arabic/Hindi leader… 90%

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▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 90%Apache-2.0
prior · pull toward the middle▇▇▇ 10%
weighted average= 48.5

$ 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