[whatmodel.sh](../../README.md) / BLOOMZ 7.1B # BLOOMZ 7.1B BigScience · released 2022-11 · 7.07B params · bigscience-bloom-rail-1.0 **score 28.8** ±1.9 · comparison only: no current run option verified · from 7 benchmark results across 4 sources Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at _{● marks BLOOMZ 7.1B among every model on that task or test, worst to best.}

task    vs the others      rank  counts  tests
coding  █···▄·▄·●··▄   #2 of 6*     71%      1

coding · #2 of 6 · 71% of its score

test               result  vs the others in its run  difficulty        counts  why
HumanEval (maker)    8.1%  ██··█··█·●·█ beats 4/5    medium · avg 59%     71%  sharp

other tests (6)

test                                result  vs the others in its run  difficulty           counts  why
DiBiMT lexical-ambiguity translat…   43.6%  █●···██···██ beats 1/5    easy · avg 74%         9.4%  blunt · at its level
WinoGrande (maker)                   55.4%  █··▁▃·▁··●·▃ beats 10/12  medium · avg 68%       8.2%  noisy
Conic10K mathQA (zero-shot CoT)       0.0%  ●··▄·······▄ beats 1/3    very hard · avg 10%    5.4%  too hard for it
COPA (maker)                         83.0%  ▃··█▅▃▃▅·●▃▃ beats 10/12  easy · avg 91%         3.2%  at its level · noisy
IndoMMLU (zero-shot)                 38.0%  ▅▃▅··▃···▃▃● beats 10/10  hard · avg 37%         0.8%  blunt · too hard for it · noisy
Story Cloze Test (maker)             89.1%  █·▂··▃··▂▂●▃ beats 10/12  easy · avg 88%         0.7%  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   83% ▇▇▇▇▇▇▇▇▇▇
Towards Effective Disambiguation for Ma…        1    9% ▇
Conic10K: A Challenging Math Problem Un…        1    5% ▇
Large Language Models Only Pass Primary…        1    1% ▇
prior: pull toward the middle                        2% ▇
weighted average                                   = 28.8

_{Licences: 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)) · Conic10K: A Challenging Math Problem Understanding and Reasoning Dataset ([CC BY 4.0](../../licenses/acl-2023-findings-emnlp-427-CC-BY-4-0.txt)) · Large Language Models Only Pass Primary School Exams in Indonesia: A Comprehensive Test on IndoMMLU ([CC BY 4.0](../../licenses/acl-2023-emnlp-main-760-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-7b1) [interactive version](https://whatmodel.sh/model/bloomz-7b1) · [JSON](https://whatmodel.sh/api?format=json&all=1)