whatmodel.sh / Deita 7B v1.0

Deita 7B v1.0

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Deita 7B v1.0 is an open-weights language model from HKUST NLP, released December 2023. Its published size is 7.24B parameters. Licence: apache-2.0.

Catalogue identity notes

Maker repo hkust-nlp/deita-7b-v1.0 @de16c09: licence tag apache-2.0. 7,241,732,096 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2023-12: month the maker's Hugging Face repo was created (createdAt 2023-12-20).

40.1±1.9#531 of 719 LLMsfrom 3 benchmark results across 2 sources

$ how it compares

The LLMs ranked either side of Deita 7B v1.0, out of 828 with a score.

#modelscorewhereRAMreleased
621Nous Hermes 2 Mistral 7B DPO Nous Research40±2local~6.1GB2024-02
622Nous Hermes 2 Mixtral 8x7B DPO Nous Research40±2local~31GB2024-01
623Deita 7B v1.0 HKUST NLP40±2local~32GB2023-12
624Code Llama 34B Instruct Meta40±1local~22GB2023-08
625Code Llama 34B (base) Meta40±1local~23GB2023-08

see all LLMs · find the ones that fit your machine

$ what it's good at

chat & writing▁▁▁▃▅▄▅█▆▆▇▃ #329 of 393 * · 92% of its score · 2 tests

Beats OpenThinker3 7B, Gemma 4 E2B, Code Llama 70B Instruct here, though they rank higher overall. Trails LFM2 350M, Gemma 3 1B, Flan-T5 XXL here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
AlpacaEval 2 length-controlled16.1% ✎▁▂▂▃▆▆█▆▄▄▁▁ beats 41/122medium · avg 39%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 82%typical
AlpacaEval 1.0 win rate (vs text-davinci-003, GPT-4 judge)90.1% ✎▂▂·▆·▇▃▆█▆▄▂ beats 31/45very easy · avg 96%▇▇▇▇ 9.5%sharp · noisy
other tests1 not in a task · 6.8% of its score
testresultvs the others in its rundifficultycountswhy
Open PT-LLM Leaderboard average (9 Portuguese tasks)66.3% ✎▃▁▁▁▁▁▁▁▂█▅▁ beats 124/276medium · avg 73%▇▇▇ 6.8%at its level · noisy

$ where to run it

Other ways to run it

  1. Run it as published · needs ~28.97 GB · from huggingface.co/hkust-nlp (official)
    uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='hkust-nlp/deita-7b-v1.0', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"

Official weights and documentation: huggingface.co/hkust-nlp ↗

$ how the score was worked out · AlpacaEval 1.0 and 2.0 leader… 92%, Open Portuguese LLM Leaderboa… 7%

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
AlpacaEval 1.0 and 2.0 leaderboards (tatsu-lab/alpaca_eval repository)2▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 92%Apache-2.0
Open Portuguese LLM Leaderboard (archived)1▇▇ 7%Apache-2.0
prior · pull toward the middle▇ 2%
weighted average= 40.1

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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