Domain Capability Enhancement through Continuous Pre-training | 3B to 70B Parameter Scale | Document Understanding & OCR Enhancement | Chain-of-Thought Reasoning Support

This repository contains models presented in the paper Qianfan-OCR: A Unified End-to-End Model for Document Intelligence.

🔗 Quick Links

Repository: 💻 GitHub
Models: 🤗 Hugging Face | 🤖 ModelScope
Documentation: 📚 Cookbook | 📝 Technical Report
Blogs: 🇨🇳 中文博客 | 🇬🇧 English Blog

Model Description

Qianfan-VL is a series of general-purpose multimodal large language models enhanced for enterprise-level multimodal applications. The models offer deep optimization for high-frequency scenarios in industrial deployment while maintaining strong general capabilities.

Model Variants

Model	Parameters	Context Length	CoT Support	Best For
Qianfan-VL-3B	3B	32k	❌	Edge deployment, real-time OCR
Qianfan-VL-8B	8B	32k	✅	Server-side general scenarios, fine-tuning
Qianfan-VL-70B	70B	32k	✅	Complex reasoning, data synthesis

Architecture

Language Model:
- Qianfan-VL-3B: Based on Qwen2.5-3B
- Qianfan-VL-8B/70B: Based on Llama 3.1 architecture
- Enhanced with 3T multilingual corpus
Vision Encoder: InternViT-based, supports dynamic patching up to 4K resolution
Cross-modal Fusion: MLP adapter for efficient vision-language bridging

Key Capabilities

🔍 OCR & Document Understanding

Full-Scenario OCR: Handwriting, formulas, natural scenes, cards/documents
Document Intelligence: Layout analysis, table parsing, chart understanding, document Q&A
High Precision: Industry-leading performance on OCR benchmarks

🧮 Chain-of-Thought Reasoning (8B & 70B)

Complex chart analysis and reasoning
Mathematical problem-solving with step-by-step derivation
Visual reasoning and logical inference
Statistical computation and trend prediction

📊 Benchmark Performance

General Vision-Language Benchmarks

Benchmark	Qianfan-VL-3B	Qianfan-VL-8B	Qianfan-VL-70B	InternVL-3-8B	InternVL-3-78B	Qwen2.5-VL-7B	Qwen2.5-VL-72B
A-Bench_VAL	75.65	75.72	78.1	75.86	75.86	76.49	79.22
CCBench	66.86	70.39	80.98	77.84	70.78	57.65	73.73
SEEDBench_IMG	76.55	78.02	79.13	77.0	77.52	76.98	78.34
SEEDBench2_Plus	67.59	70.97	73.17	69.52	68.47	70.93	73.25
MMVet	48.17	53.21	67.34	80.28	78.9	70.64	75.69
MMMU_VAL	46.44	47.11	58.33	56.11	60.78	51.0	65.78
ScienceQA_TEST	95.19	97.62	98.76	97.97	97.17	85.47	92.51
ScienceQA_VAL	93.85	97.62	98.81	97.81	95.14	83.59	91.32
MMT-Bench_VAL	62.23	63.22	71.06	65.17	63.67	61.4	69.49
MTVQA_TEST	26.5	30.14	32.18	30.3	27.62	29.08	31.48
BLINK	49.97	56.81	59.44	55.87	51.87	54.55	63.02
MMStar	57.93	64.07	69.47	68.4	66.07	61.53	66.0
RealWorldQA	65.75	70.59	71.63	71.11	74.25	69.28	73.86
Q-Bench1_VAL	73.51	75.25	77.46	75.99	77.99	78.1	79.93
POPE	85.08	86.06	88.97	90.59	88.87	85.97	83.35
RefCOCO (Avg)	85.94	89.37	91.01	89.65	91.40	86.56	90.25

OCR & Document Understanding

Benchmark	Qianfan-VL-3B	Qianfan-VL-8B	Qianfan-VL-70B	InternVL-3-8B	InternVL-3-78B	Qwen2.5-VL-3B	Qwen2.5-VL-7B	Qwen2.5-VL-72B
OCRBench	831	854	873	881	847	810	883	874
AI2D_TEST	81.38	85.07	87.23	85.07	83.55	77.07	80.472	83.84
OCRVQA_TEST	66.15	68.98	74.06	39.03	35.58	69.24	71.02	66.8
TextVQA_VAL	80.11	82.13	84.48	82.15	83.52	79.09	84.962	83.26
DocVQA_VAL	90.85	93.54	94.75	92.04	83.82	92.71	94.91	95.75
ChartQA_TEST	81.79	87.72	89.6	85.76	82.04	83.4	86.68	87.16

Quick Start

Installation

pip install transformers accelerate torch torchvision pillow einops

Using Transformers

python

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

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):
    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

# Load model
MODEL_PATH = "baidu/Qianfan-VL-8B"  # or Qianfan-VL-3B, Qianfan-VL-70B
model = AutoModel.from_pretrained(
    MODEL_PATH,
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
).eval()
tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, trust_remote_code=True)

# Load and process image
pixel_values = load_image("./example/scene_ocr.png").to(torch.bfloat16)

# Inference
prompt = "<image>请识别图中所有文字"
with torch.no_grad():
    response = model.chat(
        tokenizer,
        pixel_values=pixel_values,
        question=prompt,
        generation_config={"max_new_tokens": 512},
        verbose=False
    )
print(response)

Training Details

Four-Stage Progressive Training

Cross-modal Alignment (100B tokens): Establishes vision-language connections
General Knowledge Injection (3.5T tokens): Builds strong foundational capabilities
Domain Enhancement (300B tokens): Specialized OCR and reasoning capabilities
Post-training (1B tokens): Instruction following and preference alignment

Infrastructure

Trained on 5000+ Baidu Kunlun chips
Single-task parallel training with 5000 chips demonstrating unprecedented scale
90%+ scaling efficiency for large-scale distributed training
Innovative communication-computation fusion technology

Citation

If you use Qianfan-VL or Qianfan-OCR in your research, please cite:

bibtex

@misc{dong2026qianfanocr,
  title={Qianfan-OCR: A Unified End-to-End Model for Document Intelligence},
  author={Daxiang Dong and Mingming Zheng and Dong Xu and Chunhua Luo and Bairong Zhuang and Yuxuan Li and Ruoyun He and Haoran Wang and Wenyu Zhang and Wenbo Wang and Yicheng Wang and Xue Xiong and Ayong Zheng and Xiaoying Zuo and Ziwei Ou and Jingnan Gu and Quanhao Guo and Jianmin Wu and Dawei Yin and Dou Shen},
  year={2026},
  eprint={2603.13398},
  archivePrefix={arXiv},
  primaryClass={cs.CV},
  url={https://arxiv.org/abs/2603.13398}
}

@misc{qianfan-vl-2025,
  title={Qianfan-VL: Domain-Enhanced Universal Vision-Language Models},
  author={Qianfan Team},
  year={2025},
  publisher={Baidu}
}

Contact

For more information and API access, visit: Baidu AI Cloud Qianfan Platform