README.md

metadata

pipeline_tag: image-text-to-text

Skywork-R1V

🌐 Homepage | 📖 Technical Report | 💻 GitHub

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1. Model Introduction

Model Name	Vision Encoder	Language Model	HF Link
Skywork-R1V-38B	InternViT-6B-448px-V2_5	deepseek-ai/DeepSeek-R1-Distill-Qwen-32B	🤗 Link
Skywork-R1V-38B-qwq	InternViT-6B-448px-V2_5	Qwen/QwQ-32B	-

2. Feature

• Visual Chain-of-Thought: Enables multi-step logical reasoning on visual inputs, breaking down complex image-based problems into manageable steps.
• Mathematical & Scientific Analysis: Capable of solving visual math problems and interpreting scientific/medical imagery with high precision.
• Cross-Modal Understanding: Seamlessly integrates text and images for richer, context-aware comprehension.

3. Evaluation

Comparison with Larger-Scale Open-Source and Closed-Source Models

	Benchmark	LLM	VLM
		QwQ-32B-Preview	InternVL-2.5-38B	VILA 1.5-40B	InternVL2-40B	Skywork-R1V-38B
Reasoning	MATH-500	90.6	-	-	-	94.0
	AIME 2024	50.0	-	-	-	72.0
	GPQA	54.5	-	-	-	61.6
Vision	MathVista(mini)	-	71.9	49.5	63.7	67.5
	MMMU(Val)	-	63.9	55.1	55.2	69.0

Evaluation results of state-of-the-art LLMs and VLMs

	Vision	Reasoning			Vision
		MATH-500	AIME 2024	GPQA	MathVista(mini)	MMMU(Val)
		pass@1	pass@1	pass@1	pass@1	pass@1
Qwen2.5-72B-Instruct	❌	80.0	23.3	49.0	-	-
Deepseek V3	❌	90.2	39.2	59.1	-	-
Deepseek R1	❌	97.3	79.8	71.5	-	-
Claude 3.5 Sonnet	✅	78.3	16.0	65.0	65.3	66.4
GPT-4o	✅	74.6	9.3	49.9	63.8	69.1
Kimi k1.5	✅	96.2	77.5	-	74.9	70.0
Qwen2.5-VL-72B-Instruct	✅	-	-	-	74.8	70.2
LLaVA-Onevision-72B	✅	-	-	-	67.5	56.8
InternVL2-Llama3-76B	✅	-	-	-	65.5	62.7
InternVL2.5-78B	✅	-	-	-	72.3	70.1
Skywork-R1V-38B	✅	94.0	72.0	61.6	67.5	69.0

---

4. Usage

import math
import torch
import torchvision.transforms as T
from PIL import Image
from torchvision.transforms.functional import InterpolationMode
from transformers import AutoConfig, AutoModel, AutoTokenizer

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
    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])
    target_aspect_ratio = find_closest_aspect_ratio(
        aspect_ratio, target_ratios, orig_width, orig_height, image_size)
    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]
    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_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):
    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_path):
    device_map = {}
    world_size = torch.cuda.device_count()
    config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
    num_layers = config.llm_config.num_hidden_layers
    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

path = 'Skywork/Skywork-R1V-38B'
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)
generation_config = dict(max_new_tokens=64000, do_sample=True, temperature=0.6, top_p=0.95, repetition_penalty=1.05)

# single-image conversation
pixel_values = load_image('./examples/image1.jpg', max_num=12).to(torch.bfloat16).cuda()
question = '<image>\nSelect the correct option from this question.'
response = model.chat(tokenizer, pixel_values, question, generation_config)
print(f'User: {question}\nAssistant: {response}')

# multi-image conversation 
pixel_values1 = load_image('./examples/image1.jpg', max_num=12).to(torch.bfloat16).cuda()
pixel_values2 = load_image('./examples/image2.jpg', max_num=12).to(torch.bfloat16).cuda()
pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0)
num_patches_list = [pixel_values1.size(0), pixel_values2.size(0)]
question = '<image>\n<image>\nSelect the correct option from this question.'
response, history = model.chat(tokenizer, pixel_values, question, generation_config,
                               num_patches_list=num_patches_list,
                               history=None, return_history=True)
print(f'User: {question}\nAssistant: {response}')

---

5. Citation

If you use Skywork-R1V in your research, please cite:

@article{skywork2025r1v,
  title     = {Skywork R1V: Pioneering Multimodal Reasoning with Chain-of-Thought},
  author    = {Yi Peng, Chris, Xiaokun Wang, Yichen Wei, Jiangbo Pei, Weijie Qiu, Ai Jian, Yunzhuo Hao, Jiachun Pan, Tianyidan Xie, Li Ge, Rongxian Zhuang, Xuchen Song, Yang Liu, Yahui Zhou},
  year      = {2025},
  journal   = {https://github.com/SkyworkAI/Skywork-R1V/blob/main/Skywork_R1V.pdf},
  url       = {https://huggingface.co/Skywork/Skywork-R1V-38B}
}

This project is released under an open-source license.