[whatmodel.sh](../../README.md) / BLOOM 176B
# BLOOM 176B
BigScience · released 2022-07 · 176.2B params · bigscience-bloom-rail-1.0
**score 28.6** ±1.1 · #673 of 732 · from 16 benchmark results across 7 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks BLOOM 176B among every model on that task or test, worst to best.
task vs the others rank counts tests
coding █··█·█·██··● #1 of 6* 20% 1
coding · #1 of 6 · 20% of its score
test result vs the others in its run difficulty counts why
HumanEval (maker) 15.5% ▄···▄·▄·█··● beats 5/5 easy · avg 79% 20% sharp
other tests (10)
test result vs the others in its run difficulty counts why
ARC Challenge 45.0% █▃··▃··▅▃█▅● beats 14/14 easy · avg 81% 27% sharp
WinoGrande (maker) 48.6% ●··▁▃·▁··▁·▃ beats 1/12 easy · avg 87% 12% at its level · several runs
CommonsenseQA 42.8% █·····▂▂●▂▄▄ beats 19/38 very easy · avg 93% 9.2% sharp · at its level · small run
ARC Easy 67.5% ▅▃▃▃▃▃▃▃▅·●▃ beats 13/14 easy · avg 83% 7.3% blunt · at its level
Story Cloze Test 76.9% ▂▂▄▂·▂·▆·▂●▂ beats 11/14 easy · avg 89% 6.3% blunt · at its level · several runs
PIQA 79.2% ▄·█▄·█▄▄▄█●█ beats 12/14 easy · avg 88% 6.0% blunt · at its level · several runs
LAMBADA (OpenAI version), last-wo… 67.4% ▄▄▄█▄·█·▄●██ beats 9/14 very easy · avg 87% 4.7% blunt · at its level · several runs
DiBiMT lexical-ambiguity translat… 49.3% ██···●█···██ beats 2/5 easy · avg 74% 3.1% blunt · at its level
HellaSwag 55.9% ▅●······▃▃·▅ beats 2/8 easy · avg 86% 3.0% at its level
COPA (maker) 55.0% ▃··●▅▃▃▅·▃▃▃ beats 2/12 easy · avg 91% 1.7% at its level · noisy · several runs
source results share of the score
GPTQ: Accurate Post-Training Quantizati… 5 47% ▇▇▇▇▇▇
BLOOM: A 176B-Parameter Open-Access Mul… 1 20% ▇▇
Outlier Suppression+: Accurate quantiza… 4 16% ▇▇
GLM-130B: An Open Bilingual Pre-trained… 1 9% ▇
Crosslingual Generalization through Mul… 3 4% ▇
Towards Effective Disambiguation for Ma… 1 3% ▇
From Test-Taking to Test-Making: Examin… 1 1% ▇
prior: pull toward the middle 1% ▇
weighted average = 28.6
Licences: GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers ([CC BY 4.0](../../licenses/paper-2210.17323v2-gptq-CC-BY-4-0.txt)) · BLOOM: A 176B-Parameter Open-Access Multilingual Language Model ([CC BY 4.0](../../licenses/paper-2211.05100v4-CC-BY-4.0.txt)) · Outlier Suppression+: Accurate quantization of large language models by equivalent and effective shifting and scaling ([CC BY 4.0](../../licenses/acl-2023-emnlp-main-102-CC-BY-4-0.txt)) · GLM-130B: An Open Bilingual Pre-trained Model ([CC BY 4.0](../../licenses/paper-2210.02414v2-glm130b-CC-BY-4-0.txt)) · Crosslingual Generalization through Multitask Finetuning ([CC BY 4.0](../../licenses/acl-2023-acl-long-891-CC-BY-4-0.txt)) · Towards Effective Disambiguation for Machine Translation with Large Language Models ([CC BY 4.0](../../licenses/acl-2023-wmt-1-44-CC-BY-4-0.txt)) · From Test-Taking to Test-Making: Examining LLM Authoring of Commonsense Assessment Items ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/))
## How to run it
### Other ways to run it
1. Run it as published (BF16, needs ~352.49 GB, from [huggingface.co/bigscience](https://huggingface.co/bigscience/bloom/tree/7f10a99ce7c08f03c7719a586cb2cbda1433ac05))
```sh
uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='bigscience/bloom', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"
```
Model repository and documentation: [huggingface.co/bigscience](https://huggingface.co/bigscience/bloom)
[interactive version](https://whatmodel.sh/model/bloom) · [JSON](https://whatmodel.sh/api?format=json&all=1)