[whatmodel.sh](../../README.md) / Flan-PaLM 8B # Flan-PaLM 8B Google · released 2022-10 · 8.63B params · proprietary **score 37.4** ±1.4 · comparison only: no current run option verified · from 7 benchmark results across 2 sources · † 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 Flan-PaLM 8B among every model on that task or test, worst to best.}

task  vs the others          rank  counts  tests
math  ▁▁▃●██▅█▃▁▁▁   #462 of 530*     17%      1

math · #462 of 530 · 17% of its score

Beats [Command R Plus (04-2024)](command-r-plus-04-2024.md), [Yi 34B Chat](yi-34b-chat.md), [Llama 3 8B Instruct](llama-3-8b.md) here, though they rank higher overall. Trails [GPT-Neo 1.3B](gpt-neo-1-3b.md), [OPT-1.3B](opt-1-3b.md), [OPT-2.7B](opt-2-7b.md) here, though they rank lower overall.

test          result  vs the others in its run  difficulty        counts  why
MGSM (maker)    8.2%  ▆·▂·▄▂●▂▂█▄▆ beats 7/18   medium · avg 48%     17%  sharp

other tests (6)

test                                result  vs the others in its run  difficulty        counts  why
MMLU (maker)                         49.3%  ▁···▁··▃▁●▅█ beats 5/18   easy · avg 74%       30%  at its level
BIG-Bench Hard (maker)               36.4%  ▂▂▂▂·●·▃▂█▃▃ beats 4/18   medium · avg 60%     17%  sharp
MedQA USMLE 4-option (few-shot pr…   35.4%  ▄······●▄▄·█ beats 1/5    medium · avg 58%     11%  typical
TyDiQA-GoldP (1-shot, EM, 8-langu…   47.5%  ▂··▂·▂▄▄·█●█ beats 11/18  medium · avg 61%     10%  at its level
MedMCQA (few-shot prompted) (make…   34.5%  ▄·····●··█·█ beats 1/5    medium · avg 56%     10%  typical
PubMedQA (few-shot prompted) (mak…   67.6%  ▄·······▄▄●█ beats 3/5    medium · avg 75%    4.0%  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
Scaling Instruction-Finetuned Language …        4   74% ▇▇▇▇▇▇▇▇▇
Large Language Models Encode Clinical K…        3   25% ▇▇▇
prior: pull toward the middle                        1% ▇
weighted average                                   = 37.4

_{Licences: Scaling Instruction-Finetuned Language Models ([CC BY 4.0](../../licenses/paper-2210.11416v5-CC-BY-4.0.txt)) · Large Language Models Encode Clinical Knowledge ([CC BY 4.0](../../licenses/paper-2212.13138v1-CC-BY-4-0.txt))} ## How to run it No verified API or local download yet. [interactive version](https://whatmodel.sh/model/flan-palm-8b) · [JSON](https://whatmodel.sh/api?format=json&all=1)