[whatmodel.sh](../../README.md) / Alpaca 13B # Alpaca 13B Stanford CRFM · released 2023-03 · 13B params · closed weights **score 29.1** ±1.6 · comparison only: no current run option verified · from 4 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 Alpaca 13B among every model on that task or test, worst to best.}

task            vs the others          rank  counts  tests
chat & writing  ▁▁●▃▅▄▇█▇▇▇▃    #457 of 474     69%      3
math            ▁▁▃●██▅█▃▁▁▁   #468 of 530*     30%      1

chat & writing · #457 of 474 · 69% of its score

Beats [Phi-2 (2.7B)](phi-2.md), [Falcon 40B Instruct](falcon-40b-instruct.md), [ChatGLM3-6B](chatglm3-6b.md) here, though they rank higher overall. Trails [Gemma 3 270M IT](gemma-3-270m.md), [SmolLM2 360M Instruct](smollm2-360m.md), [Granite 4.0 350M](granite-4-0-350m.md) here, though they rank lower overall.

test                                                      result  vs the others in its run   difficulty                counts  why
LMArena (text)                                            1070.3  ▁▁●▂▃▃▃▆▆▇█▃ beats 11/385  avg 1260                     26%  blunt
Polyrating rating (bias-aware re-…                         955.0  ▂▁●▁▃▇█▆▇▄▂▂ beats 10/95   avg 1187                     24%  typical
MT-Bench (GPT-4 single-answer gra…  4.5300 MT-Bench score (1-10)  ▆▂▂●▆▄██▂▄▄▆ beats 7/28    avg 8.539 MT-Bench scor…     19%  sharp · noisy

math · #468 of 530 · 30% of its score

Beats [Yi 34B Chat](yi-34b-chat.md), [Llama 3 8B Instruct](llama-3-8b.md), [Command R (03-2024)](command-r-03-2024.md) here, though they rank higher overall. Trails [GPT-Neo 125M](gpt-neo-125m.md), [GPT-Neo 1.3B](gpt-neo-1-3b.md), [OPT-1.3B](opt-1-3b.md) here, though they rank lower overall.

test   result  vs the others in its run  difficulty      counts  why
GSM8K   11.1%  █··●······▄▄ beats 3/5    easy · avg 67%     30%  sharp

## 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
INFORM : Information eNtropy based mult…        1   30% ▇▇▇▇
LMArena leaderboard dataset                     1   26% ▇▇▇
Polyrating: A Cost-Effective and Bias-A…        1   24% ▇▇▇
LMSYS Chatbot Arena leaderboard Space: …        1   19% ▇▇
prior: pull toward the middle                        1% ▇
weighted average                                   = 29.1

_{Licences: INFORM : Information eNtropy based multi-step reasoning FOR large language Models ([CC BY 4.0](../../licenses/acl-2023-emnlp-main-216-CC-BY-4-0.txt)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Polyrating: A Cost-Effective and Bias-Aware Rating System for LLM Evaluation ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · LMSYS Chatbot Arena leaderboard Space: MT-Bench column of the published leaderboard table ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0))} ## How to run it No verified API or local download yet. [interactive version](https://whatmodel.sh/model/alpaca-13b) · [JSON](https://whatmodel.sh/api?format=json&all=1)