Image-Text-to-Text
PEFT
Safetensors
English
lora
vise
self-evolving
multimodal
vision-language
lmm
visual-grounding
image-captioning
qwen3-vl
unsupervised
conversational
Instructions to use shravvvv/VISE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use shravvvv/VISE with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-VL-2B-Instruct") model = PeftModel.from_pretrained(base_model, "shravvvv/VISE") - Notebooks
- Google Colab
- Kaggle
Add arXiv paper link
Browse files
README.md
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# VISE: Visual Invariance Self-Evolution
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This is the VISE LoRA adapter for `Qwen/Qwen3-VL-2B-Instruct`, from our paper
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VISE is a purely unsupervised, single-model self-evolving framework. Instead of
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optimizing answer agreement like prior self-evolving LMMs, it strengthens the
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# VISE: Visual Invariance Self-Evolution
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This is the VISE LoRA adapter for `Qwen/Qwen3-VL-2B-Instruct`, from our paper
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[Paying More Attention to Visual Tokens in Self-Evolving Large Multimodal Models](https://arxiv.org/abs/2606.27373).
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VISE is a purely unsupervised, single-model self-evolving framework. Instead of
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optimizing answer agreement like prior self-evolving LMMs, it strengthens the
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