whatmodel.sh / Tk-Instruct 11B (def-pos)

Tk-Instruct 11B (def-pos) is an open-weights language model from AllenAI / UW, released May 2022. Its published size is 11B parameters. Licence: apache-2.0.

Catalogue identity notes

Tk-Instruct 11B trained with definition + 2 positive examples (the paper's default Tk-Instruct); repo allenai/tk-instruct-11b-def-pos created 2022-05-05.

Model information sources

Release date ↗

42.9±5.7comparison only: no current run option verifiedfrom 2 benchmark results across 2 sources* thinly tested, treat the rank as provisional

Score within the LMArena (text) comparison set only.

$ how it compares

The LLMs ranked either side of Tk-Instruct 11B (def-pos), out of 1182 with a score, then more from AllenAI / UW.

#modelscorewhereRAMreleased
864DeepSeek LLM 67B Chat DeepSeek43±1local~31GB2023-11
865Qwen1.5 14B Chat Alibaba43±1local~11GB2024-02
866Tk-Instruct 11B (def-pos) AllenAI / UW43±6comparison only2022-05
867Llama 2 70B (base) Meta43±1local~32GB2023-07
868Mixtral 8x7B v0.1 (base) Mistral AI43±1local~29GB2023-12
more from AllenAI / UW
890Tk-Instruct 11B (def-pos-neg-expl) AllenAI / UW42±10comparison only2022-05
1109Tk-Instruct 11B (def) AllenAI / UW26±4comparison only2022-05

see all LLMs · find the ones that fit your machine

$ what it's good at

other tests2 not in a task · 86% of its score
testresultvs the others in its rundifficultycountswhy
Super-NaturalInstructions English test (definition + 2 positive examples), ROUGE-L lab62.0% ✎█····█··█··█ beats 3/3easy · avg 70%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 64%at its level
LMentry accuracy (zero-shot)49.9% ✎▂▆·▄▄█▂·▂··▂ beats 10/14medium · avg 65%▇▇▇▇▇▇▇▇▇ 22%blunt · at its level

$ where to run it

No verified API or download recorded yet · add one

Official weights and documentation: huggingface.co/allenai ↗

$ how the score was worked out · Super-NaturalInstructions: Ge… 64%, LMentry: A Language Model Ben… 22%

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
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks — authors' own runs · lab-reported1CC BY 4.0
LMentry: A Language Model Benchmark of Elementary Language Tasks — authors' own runs1CC BY 4.0
prior · pull toward the middle
weighted average= 42.9

$ improve this page

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