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Bielik 1.5B v3.0 Instruct

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Bielik 1.5B v3.0 Instruct is an open-weights language model from SpeakLeash, released April 2025. Its published size is 1.6B parameters. Licence: apache-2.0.

Catalogue identity notes

Maker repo speakleash/Bielik-1.5B-v3.0-Instruct @1907cec: licence tag apache-2.0. 1,596,507,648 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2025-04: month the maker's Hugging Face repo was created (createdAt 2025-04-18). Gated repository (licence acceptance required on Hugging Face).

39.5±3.2#538 of 719 LLMsfrom 3 benchmark results across 2 sources

$ how it compares

The LLMs ranked either side of Bielik 1.5B v3.0 Instruct, out of 828 with a score, then more from SpeakLeash.

#modelscorewhereRAMreleased
628Code Tulu 2 13B Allen Institute for AI40±2local~29GB2023-11
629LLaVA-v1.6-34B LLaVA40±2local~24GB2024-01
630Bielik 1.5B v3.0 Instruct SpeakLeash40±3local~2.4GB2025-04
631OpenHermes 2.5 Mistral 7B Nous Research39±1local~6.1GB2023-10
632Granite 3.3 2B Instruct IBM39±1local~3GB2025-04
more from SpeakLeash
325Bielik 11B v3.0 Instruct SpeakLeash55±2local~8.1GB2025-11
338Bielik 11B v2.6 Instruct SpeakLeash54±3local~8.1GB2025-06
371Bielik 11B v2.1 Instruct SpeakLeash53±3local~8.5GB2024-08

see all LLMs · find the ones that fit your machine

$ what it's good at

other tests3 not in a task · 96% of its score
testresultvs the others in its rundifficultycountswhy
CPTUB average (SpeakLeash, 0-5 judge scale) lab47.3% ✎▁▂▂▃▃█▃▆▅▄▂▃ beats 15/95easy · avg 66%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 68%at its level
EuroEval Life in the UK (English knowledge MC), test split, 5-shot, accuracy71.8% ✎▁▁▁▁▁▁▁▂▄█▄▃ beats 41/146easy · avg 83%▇▇▇▇▇▇ 14%at its level · noisy
EuroEval HellaSwag (English), test split, 5-shot generative MC, accuracy50.6% ✎▃·▁▁▂▁▃▅█▇▄▁ beats 27/100medium · avg 68%▇▇▇▇▇▇ 14%at its level · 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 1.5B v3.0 Instruct · needs ~1 GB · from huggingface.co/second-state
    chat in the terminal
    llama cli -hf second-state/Bielik-1.5B-v3.0-Instruct-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf second-state/Bielik-1.5B-v3.0-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 1.5B v3.0 Instruct · needs ~1 GB · from huggingface.co/second-state

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/second-state/Bielik-1.5B-v3.0-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 1.5B v3.0 Instruct · needs ~1 GB · from huggingface.co/second-state
    chat in the terminal
    ollama run hf.co/second-state/Bielik-1.5B-v3.0-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 1.5B v3.0 Instruct · needs ~0.9 GB, Mac only · from huggingface.co/futurist-ai
    chat in the terminal
    mlx_lm.chat --model futurist-ai/Bielik-1.5B-v3.0-Instruct-MLX-4bit
  3. Or start an OpenAI-compatible server at http://localhost:8080/v1
    mlx_lm.server --model futurist-ai/Bielik-1.5B-v3.0-Instruct-MLX-4bit

Other ways to run it

  1. Run it as published · needs ~3.19 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-1.5B-v3.0-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… 68%, EuroEval leaderboards (result… 28%

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▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 68%MIT
EuroEval leaderboards (results collection), English generative leaderboard2▇▇▇▇▇▇▇▇ 28%MIT
prior · pull toward the middle▇ 4%
weighted average= 39.5

$ 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