whatmodel.sh / PLLuM-8x7B-nc-chat (2412)

PLLuM-8x7B-nc-chat (2412)

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PLLuM-8x7B-nc-chat (2412) is an open-weights language model from PLLuM consortium (CYFRAGOVPL), released February 2025. Its published size is 46.7B parameters (12.9B active per token). Licence: cc-by-nc-4.0.

Catalogue identity notes

Maker repo CYFRAGOVPL/PLLuM-8x7B-nc-chat-2412 @39f0038 (requested as CYFRAGOVPL/PLLuM-8x7B-nc-chat, which the HF API redirects to CYFRAGOVPL/PLLuM-8x7B-nc-chat-2412): licence tag cc-by-nc-4.0. 46,702,817,280 parameters from the Hugging Face safetensors metadata; active 12.9B per token (Mixtral 8x7B architecture (MixtralForCausalLM); Mistral: 12.9B parameters per token). Released 2025-02: month the maker's Hugging Face repo was created (createdAt 2025-02-07). Gated repository (licence acceptance required on Hugging Face).

46.6±3.8#536 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 PLLuM-8x7B-nc-chat (2412), out of 1132 with a score, then more from PLLuM consortium (CYFRAGOVPL).

#modelscorewhereRAMreleased
677CogVLM2 Llama3 Chinese Chat 19B Zhipu AI (THUDM)47±7comparison only2024-05
678InternLM2.5 7B Chat 1M Shanghai AI Laboratory (InternLM)47±4local~6.4GB2024-06
679PLLuM-8x7B-nc-chat (2412) PLLuM consortium (CYFRAGOVPL)47±4local~31GB2025-02
680Yi-1.5 9B Chat 16K 01.AI47±4local~7.1GB2024-05
681Mistral7B-PairRM-SPPO UCLA AGI (SPPO)47±2local~6.1GB2024-05
more from PLLuM consortium (CYFRAGOVPL)
423PLLuM-12B-nc-chat (2507) PLLuM consortium (CYFRAGOVPL)55±4local~27GB2025-07
474Llama-PLLuM-70B-instruct (2412) PLLuM consortium (CYFRAGOVPL)53±3local~29GB2025-02
484Llama-PLLuM-70B-chat (2412) PLLuM consortium (CYFRAGOVPL)53±4local~29GB2025-02

see all LLMs · find the ones that fit your machine

$ what it's good at

other tests1 not in a task · 93% of its score
testresultvs the others in its rundifficultycountswhy
CPTUB average (SpeakLeash, 0-5 judge scale)60.6% ✎▁▂▂▂▃█▃▆▅▄▂▃ beats 34/95easy · avg 66%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 93%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 PLLuM-8x7B-nc-chat (2412) · needs ~28.4 GB · from huggingface.co/mradermacher
    chat in the terminal
    llama cli -hf mradermacher/PLLuM-8x7B-nc-chat-2412-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf mradermacher/PLLuM-8x7B-nc-chat-2412-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 PLLuM-8x7B-nc-chat (2412) · needs ~28.4 GB · from huggingface.co/mradermacher

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/mradermacher/PLLuM-8x7B-nc-chat-2412-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 PLLuM-8x7B-nc-chat (2412) · needs ~28.4 GB · from huggingface.co/mradermacher
    chat in the terminal
    ollama run hf.co/mradermacher/PLLuM-8x7B-nc-chat-2412-GGUF:Q4_K_M

Other ways to run it

  1. Run it as published · needs ~93.41 GB · from huggingface.co/CYFRAGOVPL
    uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='CYFRAGOVPL/PLLuM-8x7B-nc-chat-2412', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"

Weights and documentation: huggingface.co/CYFRAGOVPL ↗

$ how the score was worked out · SpeakLeash CPTUB (Complex Pol… 93%

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
SpeakLeash CPTUB (Complex Polish Text Understanding Benchmark)1▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 93%MIT
prior · pull toward the middle▇▇ 7%
weighted average= 46.6

$ 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