whatmodel.sh / Alpaca 13B

Alpaca 13B

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Alpaca 13B is a closed-weights language model from Stanford CRFM, released March 2023. Its published size is 13B parameters.

Catalogue identity notes

Comparison-only. Arena entry 'alpaca-13b' (text). LMArena's leaderboard table (arena-leaderboard space @6f2eeeb) labels 'alpaca-13b' as 'Alpaca-13B' (Stanford, Non-commercial), linking https://crfm.stanford.edu/2023/03/13/alpaca.html. Parameters: nominal size from the model name. Released 2023-03: Stanford CRFM Alpaca post dated 2023-03-13 (linked by LMArena's leaderboard table). LMArena's table labels it 'Alpaca-13B' (Stanford, Non-commercial) linking crfm.stanford.edu/2023/03/13/alpaca.html; FastChat's registry: 'A model fine-tuned from LLaMA on instruction-following demonstrations by Stanford'. No official 13B weights are published, so no run option.

25.8±2.0comparison only: no current run option verifiedfrom 3 benchmark results across 3 sources

$ how it compares

The LLMs ranked either side of Alpaca 13B, out of 828 with a score.

#modelscorewhereRAMreleased
772Granite 4.0 350M IBM26±1local~1.7GB2025-10
773GPT-3 curie (API base model) OpenAI26±1comparison only2020-06
774Alpaca 13B Stanford CRFM26±2comparison only2023-03
775Falcon 7B Instruct TII25±2local~5.9GB2023-04
776Qwen2 0.5B Instruct Alibaba25±2local~1.8GB2024-06

see all LLMs · find the ones that fit your machine

$ what it's good at

chat & writing▁▁▃▃▅▄▆█▇▇▇▃ #447 of 463 · 98% of its score · 3 tests

Beats Phi-2 (2.7B), Falcon 40B Instruct, ChatGLM3-6B here, though they rank higher overall. Trails Gemma 3 270M IT, SmolLM2 360M Instruct here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
Polyrating rating (bias-aware re-fit of Chatbot Arena votes, mid-2024)955.0 ✎▂▁▃▁▃▇█▆▇▄▂▂ beats 10/95avg 1201▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 34%blunt
LMArena (text)1070.1 ✎▁▁▃▂▃▃▃▆▆▇█▃ beats 11/379avg 1268▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 34%blunt
MT-Bench (GPT-4 single-answer grading, LMSYS runs)4.5300 MT-Bench score (1-10) ✎▆▂▂▆▆▄██▂▄▄▆ beats 7/28avg 8.819 MT-Bench score (1-10)▇▇▇▇▇▇▇▇▇▇▇▇ 30%sharp · noisy

$ where to run it

No verified API or download recorded yet · add one

$ how the score was worked out · Polyrating: A Cost-Effective… 34%, LMArena leaderboard dataset 34%, LMSYS Chatbot Arena leaderboa… 30%

Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The 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's added up by who ran the tests. How it works

sourceresultsshare of the scorelicence
Polyrating: A Cost-Effective and Bias-Aware Rating System for LLM Evaluation (arXiv:2409.00696v3)1▇▇▇▇▇▇▇▇▇▇ 34%CC-BY-4.0
LMArena leaderboard dataset1▇▇▇▇▇▇▇▇▇▇ 34%CC BY 4.0
LMSYS Chatbot Arena leaderboard Space: MT-Bench column of the published leaderboard table1▇▇▇▇▇▇▇▇▇ 30%Apache-2.0
prior · pull toward the middle▇ 2%
weighted average= 25.8

$ improve this page

Update model informationAdd a place to run itAdd a test resultPropose a benchmarkCorrect a benchmark scoreEdit data on GitHub

Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.

data updated · sources and licences · json