Instructions to use Ritesh-hf/Ask-Solve-Generate-VARGPT-v1.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Ritesh-hf/Ask-Solve-Generate-VARGPT-v1.1 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("VARGPT-family/VARGPT-v1.1") model = PeftModel.from_pretrained(base_model, "Ritesh-hf/Ask-Solve-Generate-VARGPT-v1.1") - Notebooks
- Google Colab
- Kaggle
Ask, Solve, Generate - VARGPT-v1.1
This repository contains the released self-evolved VARGPT-v1.1 adapters for:
Ask, Solve, Generate: Self-Evolving Unified Multimodal Understanding and Generation via Self-Consistency Rewards
The adapters were trained with the Ask-Solve-Generate self-evolving recipe using a 10k-image unlabeled pool. The base model remains frozen; this release contains the PEFT adapters used by the public codebase.
Links
- Paper: https://arxiv.org/abs/2606.27376
- Code: https://github.com/mbzuai-oryx/Ask-Solve-Generate
- Project page: https://mbzuai-oryx.github.io/Ask-Solve-Generate/
Contents
adapter_model.safetensorsandadapter_config.json: generator/default adapter.proposer/: proposer LoRA adapter.solver/: solver LoRA adapter.se_adapter_manifest.json: role-to-adapter mapping used by the training and evaluation code.
Training resume state, optimizer state, logs, generated samples, and the private data-construction pipeline are not included.
Usage
Install the public codebase, then pass this snapshot as CHECKPOINT_DIR.
git clone https://github.com/mbzuai-oryx/Ask-Solve-Generate.git
cd Ask-Solve-Generate/vargpt_1_1/VARGPT-family-training
For generation evaluation:
CHECKPOINT_DIR=/path/to/this/snapshot \
ADAPTER=generator \
bash run_scripts/run_eval_vargpt_generation_our.sh
For understanding evaluation:
cd ../understand_eval
CHECKPOINT_DIR=/path/to/this/snapshot \
ADAPTER=solver \
bash understanding_eval_our.sh
Citation
@article{thawkar2026asksolvegenerate,
title={Ask, Solve, Generate: Self-Evolving Unified Multimodal Understanding and Generation via Self-Consistency Rewards},
author={Thawkar, Ritesh and Venkatraman, Shravan and Thawakar, Omkar and Shaker, Abdelrahman and Khan, Fahad and Cholakkal, Hisham and Khan, Salman and Anwer, Rao Muhammad},
journal={arXiv preprint arXiv:2606.27376},
year={2026}
}
License
These released adapters are provided under the Apache License 2.0. Users must also comply with the terms of the upstream VARGPT-v1.1 base model and any third-party components used with it.
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Base model
VARGPT-family/VARGPT-v1.1