whatmodel.sh / Bielik 11B v2.6 Instruct

Bielik 11B v2.6 Instruct

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Bielik 11B v2.6 Instruct is an open-weights language model from SpeakLeash, released June 2025. Its published size is 11.17B parameters. Licence: apache-2.0.

Catalogue identity notes

Maker repo speakleash/Bielik-11B-v2.6-Instruct @c4104c6: licence tag apache-2.0. 11,168,796,672 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2025-06: month the maker's Hugging Face repo was created (createdAt 2025-06-02). Gated repository (licence acceptance required on Hugging Face).

54.3±2.8#284 of 719 LLMsfrom 4 benchmark results across 3 sources

$ how it compares

The LLMs ranked either side of Bielik 11B v2.6 Instruct, out of 828 with a score, then more from SpeakLeash.

#modelscorewhereRAMreleased
336Granite 4.1 8B IBM54±1api $0.11/M +local~7GB2026-04
337Pixtral Large (2411) Mistral54±3local~51GB2024-11
338Bielik 11B v2.6 Instruct SpeakLeash54±3local~8.1GB2025-06
339Nova Premier Amazon54±2api $5.00/M2025-04
340Mellum2 12B-A2.5B Thinking JetBrains54±1local~8.6GB2026-05
more from SpeakLeash
325Bielik 11B v3.0 Instruct SpeakLeash55±2local~8.1GB2025-11
371Bielik 11B v2.1 Instruct SpeakLeash53±3local~8.5GB2024-08
379Bielik 11B v2.2 Instruct SpeakLeash52±3local~8.5GB2024-08

see all LLMs · find the ones that fit your machine

$ what it's good at

other tests4 not in a task · 96% of its score
testresultvs the others in its rundifficultycountswhy
CPTUB average (SpeakLeash, 0-5 judge scale) lab72.7% ✎▁▂▂▂▃█▃▆▅▄▂▃ beats 61/95easy · avg 66%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 44%at its level
Polish EQ-Bench v2 (SpeakLeash) lab74.1% ✎▁··▁▁▂▂▆▅▆█▆ beats 65/70medium · avg 64%▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 35%at its level
EuroEval HellaSwag (English), test split, 5-shot generative MC, accuracy76.2% ✎▃·▁▁▂▁▂▅█▇▄▁ beats 63/100medium · avg 68%▇▇▇▇▇ 11%at its level · noisy
EuroEval Life in the UK (English knowledge MC), test split, 5-shot, accuracy80.2% ✎▁▁▁▁▁▁▁▂▄█▄▃ beats 64/146easy · avg 83%▇▇▇ 6.5%noisy

$ 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 Bielik 11B v2.6 Instruct · needs ~6.7 GB · from huggingface.co/speakleash (official)
    chat in the terminal
    llama cli -hf speakleash/Bielik-11B-v2.6-Instruct-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf speakleash/Bielik-11B-v2.6-Instruct-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 Bielik 11B v2.6 Instruct · needs ~6.7 GB · from huggingface.co/speakleash (official)

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/speakleash/Bielik-11B-v2.6-Instruct-GGUF@Q4_K_M -y
  4. Then run lms ls to see its name, and lms chat followed by that name

Ollama

version

  1. Install Ollama

    download it (macOS / Windows) ↗

    Linux
    curl -fsSL https://ollama.com/install.sh | sh
  2. Run Bielik 11B v2.6 Instruct · needs ~6.7 GB · from huggingface.co/speakleash (official)
    chat in the terminal
    ollama run hf.co/speakleash/Bielik-11B-v2.6-Instruct-GGUF:Q4_K_M

MLX (Mac)

version

  1. Install MLX (Mac) · Apple silicon Macs only, text only.
    with uv
    uv tool install mlx-lm
    or
    pip install mlx-lm
  2. Run Bielik 11B v2.6 Instruct · needs ~6.3 GB, Mac only · from huggingface.co/speakleash (official)
    chat in the terminal
    mlx_lm.chat --model speakleash/Bielik-11B-v2.6-Instruct-MLX-4bit
  3. Or start an OpenAI-compatible server at http://localhost:8080/v1
    mlx_lm.server --model speakleash/Bielik-11B-v2.6-Instruct-MLX-4bit

Other ways to run it

  1. Run it as published · needs ~22.34 GB · from huggingface.co/speakleash (official)
    uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='speakleash/Bielik-11B-v2.6-Instruct', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"

Official weights and documentation: huggingface.co/speakleash ↗

$ how the score was worked out · SpeakLeash CPTUB (Complex Pol… 44%, SpeakLeash Polish EQ-Bench le… 35%, EuroEval leaderboards (result… 18%

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) · lab-reported1▇▇▇▇▇▇▇▇▇▇▇▇▇ 44%MIT
SpeakLeash Polish EQ-Bench leaderboard · lab-reported1▇▇▇▇▇▇▇▇▇▇ 35%Apache-2.0
EuroEval leaderboards (results collection), English generative leaderboard2▇▇▇▇▇ 18%MIT
prior · pull toward the middle▇ 4%
weighted average= 54.3

$ 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