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BLOOM 1.7B

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BLOOM 1.7B is an open-weights language model from BigScience, released July 2022. Its published size is 1.72B parameters and a 2k-token context window. Licence: bigscience-bloom-rail-1.0.

Catalogue identity notes

BigScience BLOOM pretrained base model (not BLOOMZ).

13.8±1.4#702 of 719 LLMsfrom 12 benchmark results across 4 sources

$ how it compares

The LLMs ranked either side of BLOOM 1.7B, out of 828 with a score, then more from BigScience.

#modelscorewhereRAMreleased
808Pythia 1B EleutherAI14±1local~2.2GB2023-02
809StableLM-Tuned-Alpha 7B Stability AI14±2local~6.9GB2023-04
810BLOOM 1.7B BigScience14±1local~3.1GB2022-07
811Flan-T5 Small Google14±3local~1.8GB2022-10
812GPT-Neo 1.3B EleutherAI14±2local~7.1GB2021-03
more from BigScience
790BLOOM 7.1B BigScience21±1local~7.1GB2022-07
803BLOOM 3B BigScience18±1local~4GB2022-07
814BLOOM 1.1B BigScience11±1local~2.6GB2022-07

see all LLMs · find the ones that fit your machine

$ what it's good at

coding█···█··█··██ #3 of 5 * · 18% of its score · 1 test
testresultvs the others in its rundifficultycountswhy
HumanEval lab4.0% ✎▄···▄·▄·█··▄ beats 2/5easy · avg 78%▇▇▇▇▇▇▇ 18%sharp · too hard for it
other tests11 not in a task · 81% of its score
testresultvs the others in its rundifficultycountswhy
ARC Easy56.2% ✎▃·▅▃▃▅·██▃██ beats 7/19very easy · avg 90%▇▇▇▇▇▇▇▇ 19%at its level
SciQ85.1% ✎▂··▂▃▂▂▆▃▃█▂ beats 9/19very easy · avg 98%▇▇▇▇▇▇ 16%typical
WinoGrande57.2% ✎▄····▄▆▂▄▆▆█ beats 8/19easy · avg 89%▇▇▇▇ 10%typical
PIQA68.6% ✎▂·▄▄·▄▃▆▂▄█▄ beats 7/19easy · avg 88%▇▇▇▇ 10%blunt · at its level
LAMBADA (OpenAI version), last-word accuracy46.2% ✎▃··▅▃▃▅▅▅███ beats 6/19very easy · avg 89%▇▇▇▇ 9.1%blunt · at its level
Natural Questions (closed book)3.5% ✎▁·▁·▁▃▁▂·▅▂█ beats 3/28medium · avg 58%▇▇▇ 6.3%too hard for it
ARC Challenge23.8% ✎█··▁·▁▁·▁▂▁▂ beats 3/19medium · avg 72%▇▇ 6.2%sharp · too hard for it
TriviaQA12.7% ✎▁··▁█▃▃▁▄▅▄█ beats 8/28easy · avg 79%▇ 1.4%noisy
3 more tests
testresultvs the others in its rundifficultycountswhy
TruthfulQA MC241.3% ✎▅▃█▆▁▂▅···▁▁ beats 11/31medium · avg 64%▇ 1.2%blunt · at its level
Open PT-LLM Leaderboard average (9 Portuguese tasks)28.6% ✎▃▁▁▁▁▁▁▁▂█▅▁ beats 29/276medium · avg 73%▇ 1.0%too hard for it · noisy
HaluEval QA hallucination detection (accuracy)49.5% ✎█▁▁·▁▁·▁▁▁▁▁ beats 10/27medium · avg 70%▇ 0.2%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 1.7B · needs ~1.5 GB · from huggingface.co/mradermacher
    chat in the terminal
    llama cli -hf mradermacher/bloom-1b7-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf mradermacher/bloom-1b7-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 1.7B · needs ~1.5 GB · from huggingface.co/mradermacher

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/mradermacher/bloom-1b7-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 1.7B · needs ~1.5 GB · from huggingface.co/mradermacher
    chat in the terminal
    ollama run hf.co/mradermacher/bloom-1b7-GGUF:Q4_K_M

Other ways to run it

  1. Run it as published · needs ~3.44 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-1b7')('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… 71%, BLOOM: A 176B-Parameter Open-… 18%, Hallucinations Leaderboard re… 9% 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▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 71%CC BY-SA 4.0
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model — authors' own runs · lab-reported1▇▇▇▇▇ 18%CC BY 4.0
Hallucinations Leaderboard results (HF dataset hallucinations-leaderboard/results)4▇▇▇ 9%Apache-2.0
Open Portuguese LLM Leaderboard (archived)1▇ 1%Apache-2.0
prior · pull toward the middle▇ 1%
weighted average= 13.8

$ 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