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Artigenz-Coder-DS-6.7B

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Artigenz-Coder-DS-6.7B is an open-weights language model from Artigenz, released April 2024. Its published size is 6.74B parameters. Licence: other (deepseek).

Catalogue identity notes

Maker repo Artigenz/Artigenz-Coder-DS-6.7B @a0dea4a: licence tag other (deepseek). 6,740,512,768 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2024-04: month the maker's Hugging Face repo was created (createdAt 2024-04-15).

49.0±0.9#361 of 719 LLMsfrom 3 benchmark results across 1 source

$ how it compares

The LLMs ranked either side of Artigenz-Coder-DS-6.7B, out of 828 with a score.

#modelscorewhereRAMreleased
430Granite 4.1 3B IBM49±1local~3.7GB2026-04
431OpenCodeInterpreter-DS-6.7B M-A-P49±1local~5.8GB2024-02
432Artigenz-Coder-DS-6.7B Artigenz49±1local~5.8GB2024-04
433LFM2.5 1.2B Thinking Liquid AI49±2local~2.2GB2026-01
434Qwen3-VL 2B Thinking Alibaba49±1local~2.7GB2025-10

see all LLMs · find the ones that fit your machine

$ what it's good at

coding▁▁▁▃▆██▇▆▄▃▁ #273 of 458 · 100% of its score · 3 tests

Beats Qwen3-VL 32B Instruct, Solar Open 100B (102B-A12B), GLM-4.7-Flash here, though they rank higher overall. Trails DeepSeek LLM 67B Chat, Mistral NeMo Instruct (2407), QwQ-32B-Preview here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
HumanEval75.6% ✎▁·▁▁▃▄▆▅█▃▁▁ beats 73/99easy · avg 79%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 50%sharp
HumanEval+72.6% ✎▁·▁▁▁▃▄▄▄█▃▁ beats 81/99easy · avg 74%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 42%sharp · at its level
MBPP+69.6% ✎▁···▁▁▂▅▇█▆▂ beats 63/75easy · avg 68%▇▇▇ 8.2%at its level

$ where to run it

llama.cpp · recommended

  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 Artigenz-Coder-DS-6.7B · needs ~4.1 GB · from huggingface.co/brunopio
    chat in the terminal
    llama cli -hf brunopio/Artigenz-Coder-DS-6.7B-Q4_K_M-GGUF:q4_k_m
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf brunopio/Artigenz-Coder-DS-6.7B-Q4_K_M-GGUF:q4_k_m

LM Studio

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

    download it (macOS / Windows / Linux) ↗

  2. Run Artigenz-Coder-DS-6.7B · needs ~4.1 GB · from huggingface.co/brunopio

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/brunopio/Artigenz-Coder-DS-6.7B-Q4_K_M-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 Artigenz-Coder-DS-6.7B · needs ~4.1 GB · from huggingface.co/brunopio
    chat in the terminal
    ollama run hf.co/brunopio/Artigenz-Coder-DS-6.7B-Q4_K_M-GGUF:q4_k_m

Other ways to run it

  1. Run it as published · needs ~13.48 GB · from huggingface.co/Artigenz (official)
    uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='Artigenz/Artigenz-Coder-DS-6.7B', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"

Official weights and documentation: huggingface.co/Artigenz ↗

$ how the score was worked out · EvalPlus / EvalPerf published… 100%

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
EvalPlus / EvalPerf published evaluation runs3▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 100%Apache-2.0
prior · pull toward the middle▇ 0%
weighted average= 49.0

$ 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