| --- |
| license: cc-by-4.0 |
| datasets: |
| - jihyoung/AQuA |
| language: |
| - en |
| base_model: |
| - OpenGVLab/InternVL3-2B-Instruct |
| --- |
| |
| # AQuA-Qwen |
|
|
| <p align="center"> |
| <a href="https://arxiv.org/abs/2603.07394"><img src="https://img.shields.io/badge/arXiv-2603.07394-b31b1b?logo=arxiv&logoColor=white" alt="arXiv"/></a> |
| |
| <a href="https://huggingface.co/datasets/jihyoung/AQuA"><img src="https://img.shields.io/badge/🤗-Dataset-FFD21E?logoColor=black" alt="Dataset"/></a> |
| |
| <a href="https://huggingface.co/jihyoung/AQuA-Qwen"><img src="https://img.shields.io/badge/🤗-AQuA--Qwen-FFD21E?logoColor=black" alt="AQuA-Qwen"/></a> |
| |
| <a href="https://huggingface.co/jihyoung/AQuA-InternVL"><img src="https://img.shields.io/badge/🤗-AQuA--InternVL-FFD21E?logoColor=black" alt="AQuA-InternVL"/></a> |
| </p> |
|
|
| This model is a fine-tuned version of [InternVL3-2B-Instruct](https://huggingface.co/OpenGVLab/InternVL3-2B-Instruct) on the [AQuA dataset](https://huggingface.co/datasets/jihyoung/AQuA), trained to generate strategic responses to ambiguous visual questions. |
| |
| ## Usage |
| |
| ```python |
| import math |
| import numpy as np |
| import torch |
| import torchvision.transforms as T |
| from decord import VideoReader, cpu |
| import requests |
| from io import BytesIO |
| from PIL import Image |
| from torchvision.transforms.functional import InterpolationMode |
| from transformers import AutoModel, AutoTokenizer, AutoConfig |
| |
| IMAGENET_MEAN = (0.485, 0.456, 0.406) |
| IMAGENET_STD = (0.229, 0.224, 0.225) |
| |
| |
| def build_transform(input_size): |
| MEAN, STD = IMAGENET_MEAN, IMAGENET_STD |
| transform = T.Compose([ |
| T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img), |
| T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC), |
| T.ToTensor(), |
| T.Normalize(mean=MEAN, std=STD) |
| ]) |
| return transform |
| |
| |
| def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size): |
| best_ratio_diff = float('inf') |
| best_ratio = (1, 1) |
| area = width * height |
| for ratio in target_ratios: |
| target_aspect_ratio = ratio[0] / ratio[1] |
| ratio_diff = abs(aspect_ratio - target_aspect_ratio) |
| if ratio_diff < best_ratio_diff: |
| best_ratio_diff = ratio_diff |
| best_ratio = ratio |
| elif ratio_diff == best_ratio_diff: |
| if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]: |
| best_ratio = ratio |
| return best_ratio |
| |
| |
| def dynamic_preprocess(image, min_num=1, max_num=12, image_size=448, use_thumbnail=False): |
| orig_width, orig_height = image.size |
| aspect_ratio = orig_width / orig_height |
| |
| # calculate the existing image aspect ratio |
| target_ratios = set( |
| (i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if |
| i * j <= max_num and i * j >= min_num) |
| target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1]) |
| |
| # find the closest aspect ratio to the target |
| target_aspect_ratio = find_closest_aspect_ratio( |
| aspect_ratio, target_ratios, orig_width, orig_height, image_size) |
| |
| # calculate the target width and height |
| target_width = image_size * target_aspect_ratio[0] |
| target_height = image_size * target_aspect_ratio[1] |
| blocks = target_aspect_ratio[0] * target_aspect_ratio[1] |
| |
| # resize the image |
| resized_img = image.resize((target_width, target_height)) |
| processed_images = [] |
| for i in range(blocks): |
| box = ( |
| (i % (target_width // image_size)) * image_size, |
| (i // (target_width // image_size)) * image_size, |
| ((i % (target_width // image_size)) + 1) * image_size, |
| ((i // (target_width // image_size)) + 1) * image_size |
| ) |
| # split the image |
| split_img = resized_img.crop(box) |
| processed_images.append(split_img) |
| assert len(processed_images) == blocks |
| if use_thumbnail and len(processed_images) != 1: |
| thumbnail_img = image.resize((image_size, image_size)) |
| processed_images.append(thumbnail_img) |
| return processed_images |
| |
| |
| def load_image(image_file, input_size=448, max_num=12): |
| if isinstance(image_file, str) and image_file.startswith(('http://', 'https://')): |
| response = requests.get(image_file, timeout=10) |
| response.raise_for_status() |
| image = Image.open(BytesIO(response.content)).convert('RGB') |
| else: |
| image = Image.open(image_file).convert('RGB') |
| |
| transform = build_transform(input_size=input_size) |
| images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num) |
| pixel_values = [transform(image) for image in images] |
| pixel_values = torch.stack(pixel_values) |
| return pixel_values |
| |
| |
| def split_model(model_name): |
| device_map = {} |
| world_size = torch.cuda.device_count() |
| config = AutoConfig.from_pretrained(model_name, trust_remote_code=True) |
| num_layers = config.llm_config.num_hidden_layers |
| # Since the first GPU will be used for ViT, treat it as half a GPU. |
| num_layers_per_gpu = math.ceil(num_layers / (world_size - 0.5)) |
| num_layers_per_gpu = [num_layers_per_gpu] * world_size |
| num_layers_per_gpu[0] = math.ceil(num_layers_per_gpu[0] * 0.5) |
| layer_cnt = 0 |
| for i, num_layer in enumerate(num_layers_per_gpu): |
| for j in range(num_layer): |
| device_map[f'language_model.model.layers.{layer_cnt}'] = i |
| layer_cnt += 1 |
| device_map['vision_model'] = 0 |
| device_map['mlp1'] = 0 |
| device_map['language_model.model.tok_embeddings'] = 0 |
| device_map['language_model.model.embed_tokens'] = 0 |
| device_map['language_model.output'] = 0 |
| device_map['language_model.model.norm'] = 0 |
| device_map['language_model.model.rotary_emb'] = 0 |
| device_map['language_model.lm_head'] = 0 |
| device_map[f'language_model.model.layers.{num_layers - 1}'] = 0 |
| |
| return device_map |
| |
| |
| # If you set `load_in_8bit=True`, you will need two 80GB GPUs. |
| # If you set `load_in_8bit=False`, you will need at least three 80GB GPUs. |
| path = 'jihyoung/AQuA-InternVL' |
| device_map = split_model(path) |
| model = AutoModel.from_pretrained( |
| path, |
| torch_dtype=torch.bfloat16, |
| load_in_8bit=False, |
| low_cpu_mem_usage=True, |
| use_flash_attn=True, |
| trust_remote_code=True, |
| device_map=device_map).eval() |
| tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True, use_fast=False) |
| |
| # set the max number of tiles in `max_num` |
| pixel_values = load_image('http://images.cocodataset.org/train2017/000000539311.jpg', max_num=12).to(torch.bfloat16).cuda() |
| generation_config = dict(max_new_tokens=1024, do_sample=True) |
| |
| question = '<image>\nWhat color is this bat?' |
| response = model.chat(tokenizer, pixel_values, question, generation_config) |
| print(f'User: {question}\nAssistant: {response}') |
| ``` |
| |
| ## Citation |
| |
| If you use this model in your research, please cite: |
| |
| ```bibtex |
| @inproceedings{ |
| jang2026aqua, |
| title={{AQ}uA: Toward Strategic Response Generation for Ambiguous Visual Questions}, |
| author={Jihyoung Jang and Hyounghun Kim}, |
| booktitle={The Fourteenth International Conference on Learning Representations}, |
| year={2026}, |
| url={https://openreview.net/forum?id=7b1MpD6IF8} |
| } |
| ``` |
|
|
