[whatmodel.sh](../../README.md) / BLOOMZ 176B # BLOOMZ 176B BigScience · released 2022-11 · 176.2B params · bigscience-bloom-rail-1.0 **score 32.7** ±1.5 · comparison only: no current run option verified · from 8 benchmark results across 3 sources Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at ● marks BLOOMZ 176B among every model on that task or test, worst to best. 
task    vs the others      rank  counts  tests
coding  █···▄·▄·▄··●   #1 of 6*     57%      1
coding · #1 of 6 · 57% of its score 
test               result  vs the others in its run  difficulty        counts  why
HumanEval (maker)   12.1%  ██··█··█·█·● beats 5/5    medium · avg 59%     57%  sharp
other tests (6) 
test                                result  vs the others in its run  difficulty           counts  why
WinoGrande (maker)                   58.4%  █··▁▃·▁··▁·● beats 11/12  easy · avg 87%          25%  at its level · several runs
DiBiMT lexical-ambiguity translat…   55.2%  ██···██···●█ beats 4/5    easy · avg 74%         6.0%  blunt · at its level
HellaSwag                            57.1%  ▅●······▃▃·▅ beats 4/8    easy · avg 86%         5.9%  at its level
LAMBADA (OpenAI version), last-wo…   67.8%  ▁·····▁▁▃▃●█ beats 46/86  very easy · avg 85%    2.5%  blunt · at its level · small run
COPA (maker)                         88.0%  ▃··█▅▃▃▅·▃●▃ beats 11/12  easy · avg 91%         2.0%  at its level · noisy
Story Cloze Test (maker)             95.7%  █·▂··▃··▂▂▂● beats 12/12  easy · avg 88%         0.5%  blunt · at its level · noisy
## How the score was worked out Each result implies a score on its own, after adjusting for how hard that test is and who ran it. The model's 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 is added up by who ran the tests. [Method](../method.md) 
source                                    results  share of the score
Crosslingual Generalization through Mul…        4   66% ▇▇▇▇▇▇▇▇
Outlier Suppression+: Accurate quantiza…        3   27% ▇▇▇
Towards Effective Disambiguation for Ma…        1    6% ▇
prior: pull toward the middle                        1% ▇
weighted average                                   = 32.7
Licences: Crosslingual Generalization through Multitask Finetuning ([CC BY 4.0](../../licenses/acl-2023-acl-long-891-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)) · 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)) ## How to run it No verified API or local download yet. Model repository and documentation: [huggingface.co/bigscience](https://huggingface.co/bigscience/bloomz) [interactive version](https://whatmodel.sh/model/bloomz) · [JSON](https://whatmodel.sh/api?format=json&all=1)