whatmodel.sh / PLLuM-8x7B-instruct (2412)

PLLuM-8x7B-instruct (2412)

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PLLuM-8x7B-instruct (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: apache-2.0.

Catalogue identity notes

Maker repo CYFRAGOVPL/PLLuM-8x7B-instruct-2412 @d06a8c4 (requested as CYFRAGOVPL/PLLuM-8x7B-instruct, which the HF API redirects to CYFRAGOVPL/PLLuM-8x7B-instruct-2412): licence tag apache-2.0. 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).

46.3±3.8#548 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-instruct (2412), out of 1132 with a score, then more from PLLuM consortium (CYFRAGOVPL).

#modelscorewhereRAMreleased
690OLMo 2 13B Instruct (1124) Allen Institute for AI46±1local~9.6GB2024-12
691PLLuM-8x7B-chat (2412) PLLuM consortium (CYFRAGOVPL)46±4local~31GB2025-02
692PLLuM-8x7B-instruct (2412) PLLuM consortium (CYFRAGOVPL)46±4local~31GB2025-02
693Falcon-H1 7B Instruct TII46±3local~6GB2025-05
694GPT-3.5 Turbo (1106) OpenAI46±1comparison only2023-11
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 · 94% of its score
testresultvs the others in its rundifficultycountswhy
CPTUB average (SpeakLeash, 0-5 judge scale)60.1% ✎▁▂▂▂▃█▃▆▅▄▂▃ beats 32/95easy · avg 66%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 94%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-instruct (2412) · needs ~28.4 GB · from huggingface.co/mradermacher
    chat in the terminal
    llama cli -hf mradermacher/PLLuM-8x7B-instruct-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-instruct-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-instruct (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-instruct-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-instruct (2412) · needs ~28.4 GB · from huggingface.co/mradermacher
    chat in the terminal
    ollama run hf.co/mradermacher/PLLuM-8x7B-instruct-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-instruct-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… 94%

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▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 94%MIT
prior · pull toward the middle▇▇ 6%
weighted average= 46.3

$ 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