[whatmodel.sh](../../README.md) / Deita 7B v1.0 # Deita 7B v1.0 HKUST NLP · released 2023-12 · 7.24B params · apache-2.0 **score 41.8** ±1.8 · #535 of 732 · from 3 benchmark results across 2 sources Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at _{● marks Deita 7B v1.0 among every model on that task or test, worst to best.}

task            vs the others          rank  counts  tests
chat & writing  ▁▁▁▃●▄▆█▆▇▇▃   #335 of 402*     91%      2

chat & writing · #335 of 402 · 91% of its score

Beats [OpenThinker3 7B](openthinker3-7b.md), [Code Llama 70B Instruct](codellama-70b-instruct.md), [Gemma 4 E2B](gemma-4-e2b.md) here, though they rank higher overall. Trails [LFM2 350M](lfm2-350m.md), [Gemma 3 1B](gemma-3-1b.md), [Qwen3 0.6B](qwen3-0-6b.md) here, though they rank lower overall.

test                                result  vs the others in its run   difficulty           counts  why
AlpacaEval 2 length-controlled       16.1%  ▁▂▂▃▆●█▆▄▄▁▁ beats 41/122  medium · avg 35%        86%  sharp
AlpacaEval 1.0 win rate (vs text-…   90.1%  ▂▂·▆·▇▃▆●▆▄▂ beats 31/45   very easy · avg 94%    5.2%  sharp · noisy

other tests (1)

test                                result  vs the others in its run    difficulty        counts  why
Open PT-LLM Leaderboard average (…   66.3%  ▃▁▁▁▁▁▁▁▂●▅▁ beats 124/276  medium · avg 72%    7.8%  at its level · noisy

## How the score was worked out Each result implies a score on its own, after adjusting for how hard that test is and who ran it. The model's 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 is added up by who ran the tests. [Method](../method.md)

source                               results  share of the score
AlpacaEval 1.0 and 2.0 leaderboards        2   91% ▇▇▇▇▇▇▇▇▇▇▇
Open Portuguese LLM Leaderboard            1    8% ▇
prior: pull toward the middle                   1% ▇
weighted average                              = 41.8

_{Licences: AlpacaEval 1.0 and 2.0 leaderboards ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt))} ## How to run it ### Other ways to run it 1. Run it as published (fp32, needs ~28.97 GB, from [huggingface.co/hkust-nlp](https://huggingface.co/hkust-nlp/deita-7b-v1.0/tree/de16c0994d14f18a59fd41f1fb6cc1bc96aa1663)) ```sh 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'])" ``` Model repository and documentation: [huggingface.co/hkust-nlp](https://huggingface.co/hkust-nlp/deita-7b-v1.0) [interactive version](https://whatmodel.sh/model/deita-7b-v1-0) · [JSON](https://whatmodel.sh/api?format=json&all=1)