[whatmodel.sh](../../README.md) / LaMDA 8B (pre-trained) # LaMDA 8B (pre-trained) Google · released 2022-01 · 8B params · proprietary **score 12.2** ±1.5 · comparison only: no current run option verified · from 9 benchmark results across 1 source · † no independently run results verified Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at ● marks LaMDA 8B (pre-trained) among every model on that task or test, worst to best. 
task  vs the others          rank  counts  tests
math  ▁●▃▆██▅█▃▁▁▁   #519 of 530*    5.9%      1
math · #519 of 530 · 5.9% of its score Beats [OPT-66B](opt-66b.md), [Flan-T5 Base](flan-t5-base.md), [Flan-T5 Small](flan-t5-small.md) here, though they rank higher overall. Trails [GPT-Neo 125M](gpt-neo-125m.md), [OPT-125M](opt-125m.md), [GPT-2 XL (1.5B)](gpt-2-xl.md) here, though they rank lower overall. 
test           result  vs the others in its run  difficulty      counts  why
GSM8K (maker)    1.6%  █·●▃▃▃·▃·▃▃▅ beats 3/12   easy · avg 67%    5.9%  sharp · too hard for it
other tests (8) 
test                                result  vs the others in its run  difficulty           counts  why
MAWPS (CoT-paper union) (maker)       4.8%  ▅▃●···▅·▃▃▃▅ beats 5/12   very easy · avg 94%     38%  sharp · too hard for it
ASDiv (maker)                         5.0%  ▅▃▃●··▃▃·▃▃█ beats 5/12   easy · avg 83%          20%  sharp · too hard for it
SVAMP (maker)                         3.4%  ▃▅●···▅·▃▃▃▅ beats 5/12   easy · avg 82%          16%  sharp · too hard for it
BIG-bench Date Understanding (mak…    9.5%  ▃·▅·▅●▅·▃▃·█ beats 5/12   medium · avg 52%       7.5%  too hard for it
CommonsenseQA (maker)                19.0%  ●······▁·▁▁▃ beats 0/12   easy · avg 88%         4.2%  sharp · too hard for it
BIG-bench Sports Understanding (m…   50.0%  ●·····▃·▁·▄▁ beats 0/12   medium · avg 81%       3.0%  sharp · too hard for it
StrategyQA (maker)                   54.1%  █·····▅●▃▅▃█ beats 5/12   medium · avg 70%       2.7%  too hard for it
AQuA-RAT (maker)                     18.6%  ●▁·▁▁·····▂▁ beats 1/12   medium · avg 48%       0.5%  sharp · too hard for it
## 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
Chain-of-Thought Prompting Elicits Reas…        9   99% ▇▇▇▇▇▇▇▇▇▇▇▇
prior: pull toward the middle                        1% ▇
weighted average                                   = 12.2
Licences: Chain-of-Thought Prompting Elicits Reasoning in Large Language Models ([CC BY 4.0](../../licenses/paper-2201.11903v6-CC-BY-4-0.txt)) ## How to run it No verified API or local download yet. [interactive version](https://whatmodel.sh/model/lamda-8b) · [JSON](https://whatmodel.sh/api?format=json&all=1)