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BLOOM 560M

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BLOOM 560M is an open-weights language model from BigScience, released July 2022. Its published size is 0.559B parameters and a 2k-token context window. Licence: bigscience-bloom-rail-1.0.

Catalogue identity notes

BigScience BLOOM pretrained base model (not BLOOMZ).

5.2±1.6#711 of 719 LLMsfrom 12 benchmark results across 4 sources

$ how it compares

The LLMs ranked either side of BLOOM 560M, out of 828 with a score, then more from BigScience.

#modelscorewhereRAMreleased
818Zyte-1B venkycs (aihub-app)8±2local~2.2GB2024-01
819GPT-Neo 125M EleutherAI5±5local~2.1GB2021-03
820BLOOM 560M BigScience5±2local~2.1GB2022-07
821OPT-350M Meta5±2local~2.2GB2022-05
822StableLM-Base-Alpha 3B Stability AI4±1local~17GB2023-04
more from BigScience
790BLOOM 7.1B BigScience21±1local~7.1GB2022-07
803BLOOM 3B BigScience18±1local~4GB2022-07
810BLOOM 1.7B BigScience14±1local~3.1GB2022-07

see all LLMs · find the ones that fit your machine

$ what it's good at

coding█···█··█··██ #5 of 5 * · 9.2% of its score · 1 test
testresultvs the others in its rundifficultycountswhy
HumanEval lab0.8% ✎▄···▄·▄·█··▄ beats 0/5easy · avg 78%▇▇▇▇ 9.2%sharp · too hard for it
other tests11 not in a task · 90% of its score
testresultvs the others in its rundifficultycountswhy
SciQ80.4% ✎▂··▂▃▃▂▆▃▃█▂ beats 4/19very easy · avg 98%▇▇▇▇▇▇▇▇▇▇▇ 27%at its level
ARC Easy47.6% ✎▃·▅▃▃▅·██▃██ beats 4/19very easy · avg 90%▇▇▇▇▇▇▇▇▇ 21%at its level
PIQA63.7% ✎▂·▄▄·▄▂▆▂▄█▄ beats 3/19easy · avg 88%▇▇▇▇▇ 12%blunt · at its level
LAMBADA (OpenAI version), last-word accuracy34.1% ✎▃··▅▃▃▅▅▅███ beats 2/19very easy · avg 89%▇▇▇▇▇ 12%blunt · at its level
WinoGrande50.4% ✎▄····▄▆▂▄▆▆█ beats 1/19easy · avg 89%▇▇▇ 7.3%too hard for it
Natural Questions (closed book)1.6% ✎▁·▃·▁▂▁▂·▅▂█ beats 1/28medium · avg 58%▇▇ 4.0%too hard for it
ARC Challenge22.1% ✎█··▁·▁▁·▁▂▁▂ beats 5/19medium · avg 72%▇ 3.0%sharp · too hard for it
TruthfulQA MC242.4% ✎▅▃█▆▁▂▅···▁▁ beats 14/31medium · avg 64%▇ 1.5%blunt · at its level
3 more tests
testresultvs the others in its rundifficultycountswhy
TriviaQA5.7% ✎▁··▃█▃▃▁▄▅▄█ beats 1/28easy · avg 79%▇ 1.1%too hard for it · noisy
Open PT-LLM Leaderboard average (9 Portuguese tasks)25.4% ✎▃▁▁▁▁▁▁▁▂█▅▁ beats 28/276medium · avg 73%▇ 0.7%too hard for it · noisy
HaluEval QA hallucination detection (accuracy)50.0% ✎█▁▁·▁▁·▁▁▁▁▁ beats 9/27medium · avg 70%▇ 0.1%sharp · too hard for it · 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 BLOOM 560M · needs ~0.6 GB · from huggingface.co/afrideva
    chat in the terminal
    llama cli -hf afrideva/bloom-560m-GGUF:q4_k_m
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf afrideva/bloom-560m-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 BLOOM 560M · needs ~0.6 GB · from huggingface.co/afrideva

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/afrideva/bloom-560m-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 BLOOM 560M · needs ~0.6 GB · from huggingface.co/afrideva
    chat in the terminal
    ollama run hf.co/afrideva/bloom-560m-GGUF:q4_k_m

Other ways to run it

  1. Run it as published · needs ~1.12 GB · from huggingface.co/bigscience (official)
    uv run --with torch --with 'transformers<5' python -c "from transformers import pipeline; print(pipeline('text-generation', model='bigscience/bloom-560m')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"

Official weights and documentation: huggingface.co/bigscience ↗

$ how the score was worked out · Pythia: A Suite for Analyzing… 82%, BLOOM: A 176B-Parameter Open-… 9%, Hallucinations Leaderboard re… 7% and 1 more

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
Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling — authors' own runs6▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 82%CC BY-SA 4.0
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model — authors' own runs · lab-reported1▇▇▇ 9%CC BY 4.0
Hallucinations Leaderboard results (HF dataset hallucinations-leaderboard/results)4▇▇ 7%Apache-2.0
Open Portuguese LLM Leaderboard (archived)1▇ 1%Apache-2.0
prior · pull toward the middle▇ 1%
weighted average= 5.2

$ 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