[whatmodel.sh](../../README.md) / LLaVA-OneVision Qwen2 72B (ov-sft) # LLaVA-OneVision Qwen2 72B (ov-sft) LLaVA (lmms-lab) · released 2024-08 · 73.2B params · apache-2.0 **score 45.3** ±2.7 · #469 of 732 · from 3 benchmark results across 3 sources Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at ● marks LLaVA-OneVision Qwen2 72B (ov-sft) among every model on that task or test, worst to best. 
task                vs the others         rank  counts  tests
vision & documents  ▁▁●▂▁▃▄▂▆▇█▄   #144 of 153     97%      3
vision & documents · #144 of 153 · 97% of its score Beats [Gemma 3 4B](gemma-3-4b.md) here, though they rank higher overall. Trails [Qwen3.5 0.8B](qwen3-5-0-8b.md), [North Micro Vision Instruct (2.4B)](north-micro-vision-instruct.md), [Molmo 7B-D (0924)](molmo-7b-d.md) here, though they rank lower overall. 
test                                  result  vs the others in its run  difficulty      counts  why
Arena vision (style controlled)        977.9  ▁▁●▂▂▂▃▄▅▅█▇ beats 6/152  avg 1061           53%  blunt
CapArena-Auto detailed image capt…  -12.3300  ▃▃▃·▃●·▅▃▃▃▃ beats 4/12   avg -4.498         33%  typical
MSEarth-Bench Earth-science figur…     51.1%  █·█●····██·█ beats 2/5    hard · avg 48%     11%  blunt · at its level
## 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
LMArena leaderboard dataset                     1   53% ▇▇▇▇▇▇
CapArena: Benchmarking and Analyzing De…        1   33% ▇▇▇▇
MSEarth: A Multimodal Benchmark for Ear…        1   11% ▇
prior: pull toward the middle                        3% ▇
weighted average                                   = 45.3
Licences: LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · CapArena: Benchmarking and Analyzing Detailed Image Captioning in the LLM Era ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · MSEarth: A Multimodal Benchmark for Earth Science Phenomenon Discovery with MLLMs ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) ## How to run it ### Other ways to run it 1. Run it as published (fp16, needs ~146.36 GB, NVIDIA only, from [github.com/vllm-project](https://github.com/vllm-project/vllm/blob/64cb68384241f046d07ba3797e1b14dbc82a0b2d/docs/models/supported_models.md)) ```sh vllm serve llava-hf/llava-onevision-qwen2-72b-ov-hf ``` Model repository and documentation: [huggingface.co/lmms-lab](https://huggingface.co/lmms-lab/llava-onevision-qwen2-72b-ov-sft) [interactive version](https://whatmodel.sh/model/llava-onevision-qwen2-72b-ov) · [JSON](https://whatmodel.sh/api?format=json&all=1)