whatmodel.sh / Bielik 7B Instruct v0.1

Bielik 7B Instruct v0.1

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Bielik 7B Instruct v0.1 is an open-weights language model from SpeakLeash, released March 2024. Its published size is 7.24B parameters. Licence: cc-by-nc-4.0.

Catalogue identity notes

Maker repo speakleash/Bielik-7B-Instruct-v0.1 @ff00a0b: licence tag cc-by-nc-4.0. 7,241,732,096 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2024-03: month the maker's Hugging Face repo was created (createdAt 2024-03-30).

41.2±2.8#688 of 949 LLMsfrom 2 benchmark results across 2 sources* thinly tested, treat the rank as provisional

$ how it compares

The LLMs ranked either side of Bielik 7B Instruct v0.1, out of 1132 with a score, then more from SpeakLeash.

#modelscorewhereRAMreleased
847OpenChat 3.5 OpenChat41±2local~6.1GB2023-10
848InternVL2-4B Shanghai AI Laboratory (OpenGVLab)41±2local~10GB2024-07
849Bielik 7B Instruct v0.1 SpeakLeash41±3local~6GB2024-03
850Qwen1.5 14B Chat Alibaba41±1local~11GB2024-02
851Code Llama 34B Python Meta41±1local~23GB2023-08
more from SpeakLeash
427Bielik 11B v3.0 Instruct SpeakLeash55±2local~8.1GB2025-11
445Bielik 11B v2.6 Instruct SpeakLeash54±3local~8.1GB2025-06
473Bielik 11B v2.5 Instruct SpeakLeash53±3local~8.5GB2025-05

see all LLMs · find the ones that fit your machine

$ what it's good at

other tests2 not in a task · 96% of its score
testresultvs the others in its rundifficultycountswhy
CPTUB average (SpeakLeash, 0-5 judge scale) lab57.7% ✎▁▂▂▂▃█▃▆▅▄▂▃ beats 25/95easy · avg 66%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 54%at its level
Polish EQ-Bench v2 (SpeakLeash) lab34.3% ✎▁··▁▁▂▃▆▅▆█▆ beats 8/70medium · avg 64%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 42%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 Bielik 7B Instruct v0.1 · needs ~4.4 GB · from huggingface.co/speakleash (official)
    chat in the terminal
    llama cli -hf speakleash/Bielik-7B-Instruct-v0.1-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf speakleash/Bielik-7B-Instruct-v0.1-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 7B Instruct v0.1 · needs ~4.4 GB · from huggingface.co/speakleash (official)

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/speakleash/Bielik-7B-Instruct-v0.1-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 7B Instruct v0.1 · needs ~4.4 GB · from huggingface.co/speakleash (official)
    chat in the terminal
    ollama run hf.co/speakleash/Bielik-7B-Instruct-v0.1-GGUF:Q4_K_M

MLX (Mac)

  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 7B Instruct v0.1 · needs ~4.3 GB, Mac only · from huggingface.co/speakleash (official)
    chat in the terminal
    mlx_lm.chat --model speakleash/Bielik-7B-Instruct-v0.1-MLX
  3. Or start an OpenAI-compatible server at http://localhost:8080/v1
    mlx_lm.server --model speakleash/Bielik-7B-Instruct-v0.1-MLX

Other ways to run it

  1. Run it as published · needs ~14.48 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-7B-Instruct-v0.1', 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… 54%, SpeakLeash Polish EQ-Bench le… 42%

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▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 54%MIT
SpeakLeash Polish EQ-Bench leaderboard · lab-reported1▇▇▇▇▇▇▇▇▇▇▇▇▇ 42%Apache-2.0
prior · pull toward the middle▇ 4%
weighted average= 41.2

$ 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