whatmodel.sh / OPT-350M

OPT-350M

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OPT-350M is an open-weights language model from Meta, released May 2022. Its published size is 0.35B parameters and a 2k-token context window. Licence: opt-175b-license-noncommercial.

Catalogue identity notes

Meta's Open Pre-trained Transformer base model as released on Hugging Face (weights under Meta's non-commercial OPT licence).

4.6±1.7#712 of 719 LLMsfrom 7 benchmark results across 2 sources

$ how it compares

The LLMs ranked either side of OPT-350M, out of 828 with a score, then more from Meta.

#modelscorewhereRAMreleased
819GPT-Neo 125M EleutherAI5±5local~2.1GB2021-03
820BLOOM 560M BigScience5±2local~2.1GB2022-07
821OPT-350M Meta5±2local~2.2GB2022-05
822StableLM-Base-Alpha 3B Stability AI4±1local~17GB2023-04
823Pythia 160M EleutherAI3±2local~1.6GB2023-02
more from Meta
24Muse Spark 1.3 Meta77±1api $2.00/M2026-09
40Muse Spark 1.2 Meta75±1api $2.00/M2026-08
43Muse Spark 1.1 Meta75±1api $2.00/M2026-07

see all LLMs · find the ones that fit your machine

$ what it's good at

other tests7 not in a task · 99% of its score
testresultvs the others in its rundifficultycountswhy
SciQ74.8% ✎▂··▂▃▂▂▆▃▃█▂ beats 2/19very easy · avg 98%▇▇▇▇▇▇▇▇▇▇▇▇▇ 33%at its level
ARC Easy44.0% ✎▃·▅▃▃▅·██▃██ beats 3/19very easy · avg 90%▇▇▇▇▇▇▇▇▇▇ 25%at its level
PIQA64.4% ✎▂·▄▄·▄▂▆▂▄█▄ beats 4/19easy · avg 88%▇▇▇▇▇▇ 14%blunt · at its level
LAMBADA (OpenAI version), last-word accuracy45.2% ✎▃··▅▃▃▅▅▅███ beats 5/19very easy · avg 89%▇▇▇▇▇▇ 14%blunt · at its level
WinoGrande52.3% ✎▄····▄▆▂▄▆▆█ beats 2/19easy · avg 89%▇▇▇ 8.4%too hard for it
ARC Challenge20.7% ✎█··▁·▁▁·▁▂▁▂ beats 1/19medium · avg 72%▇ 3.3%sharp · too hard for it
Open PT-LLM Leaderboard average (9 Portuguese tasks)23.3% ✎▃▁▁▁▁▁▁▁▂█▅▁ beats 13/276medium · avg 73%▇ 0.8%too hard for it · noisy

$ where to run it

Other ways to run it

  1. Run it as published · needs ~0.66 GB · from huggingface.co/facebook
    uv run --with torch --with 'transformers<5' python -c "from transformers import pipeline; print(pipeline('text-generation', model='facebook/opt-350m')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"

Weights and documentation: huggingface.co/facebook ↗

$ how the score was worked out · Pythia: A Suite for Analyzing… 98%, Open Portuguese LLM Leaderboa… 1%

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
Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling — authors' own runs6▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 98%CC BY-SA 4.0
Open Portuguese LLM Leaderboard (archived)1▇ 1%Apache-2.0
prior · pull toward the middle▇ 1%
weighted average= 4.6

$ 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