whatmodel.sh / OLMo 7B Twin 2T

OLMo 7B Twin 2T

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OLMo 7B Twin 2T is an open-weights language model from Allen Institute for AI, released February 2024. Its published size is 6.89B parameters and a 2k-token context window. Licence: apache-2.0.

Catalogue identity notes

OLMo 7B Twin 2T (trained on LUMI/AMD hardware to 2T tokens); the -hf repo is AI2's Transformers-format conversion.

32.4±7.1#833 of 949 LLMsfrom 1 benchmark result across 1 source* thinly tested, treat the rank as provisional

$ how it compares

The LLMs ranked either side of OLMo 7B Twin 2T, out of 1132 with a score, then more from Allen Institute for AI.

#modelscorewhereRAMreleased
1002OLMo 2 1B Instruct (0425) Allen Institute for AI33±2local~2.4GB2025-04
1003Vicuna 7B v1.3 LMSYS33±2local~5.8GB2023-06
1004OLMo 7B Twin 2T Allen Institute for AI32±7local~30GB2024-02
1005DeepSeek-Coder 1.3B Base DeepSeek32±1local~2.4GB2023-10
1006Orca 2 13B Microsoft32±2local~9.8GB2023-11
more from Allen Institute for AI
690OLMo 2 13B Instruct (1124) Allen Institute for AI46±1local~9.6GB2024-12
718Tulu 2 70B Allen Institute for AI46±2local~28GB2023-11
777Code Tulu 2 34B Allen Institute for AI44±2local~72GB2023-11

see all LLMs · find the ones that fit your machine

$ what it's good at

other tests1 not in a task · 78% of its score
testresultvs the others in its rundifficultycountswhy
Open PT-LLM Leaderboard average (9 Portuguese tasks)45.0% ✎▃▁▁▁▁▁▃▁▂█▅▁ beats 59/276medium · avg 73%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 78%at its level · noisy

$ where to run it

Other ways to run it

  1. Run it as published · needs ~27.55 GB · from huggingface.co/allenai
    uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='allenai/OLMo-7B-Twin-2T-hf', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"

Weights and documentation: huggingface.co/allenai ↗

$ how the score was worked out · Open Portuguese LLM Leaderboa… 78%

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
Open Portuguese LLM Leaderboard (archived)1▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 78%Apache-2.0
prior · pull toward the middle▇▇▇▇▇▇▇ 22%
weighted average= 32.4

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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