vankey

DocShield-9B

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README

License: apache-2.0

Training data

DocShield-9B was developed using the RealText forensic document datasets:

Capabilities

Visual forgery trace analysis — crude redaction / mosaic, font & anti-aliasing inconsistency, edge halos, copy-paste artifacts, compression mismatches.
Logical & fact-checking — price/quantity contradictions, date conflicts, bulk-discount logic violations.
Semantic alteration detection — subtle spec substitutions (e.g. color, material) that bypass crude visual checks.
Localization — bounding-box coordinates for each tampered region with per-anomaly reasoning.
Structured report — conclusion (FORGED / authentic) + fraud-risk score.

Model details

Table

Base model	Qwen3.5-VL-9B
Architecture	Qwen3_5ForConditionalGeneration (hybrid linear / full attention)
Precision (weights)	bfloat16
Max new tokens	1024 (default)
Thinking mode	supported (`--thinking` / `--no-thinking`)

The base model is not bundled here. This repository only releases the fine-tuned DocShield-9B weights.

Quick start

Install dependencies:

bash
pip install -U transformers torch torchvision pillow qwen-vl-utils

Requires a transformers version with native Qwen3.5-VL (qwen3_5) support. The released weights were saved with transformers==5.13.0.

Run inference (see inference.py in this repo):

bash
# default: greedy, thinking disabled
python inference.py --image path/to/image.jpg --no-thinking --max-new-tokens 1024

# with sampling + thinking mode
python inference.py --image path/to/image.jpg --thinking --do-sample \
                    --temperature 0.6 --top-p 0.8 --top-k 20 --max-new-tokens 2048

Minimal example

python
import torch
from transformers import AutoProcessor, AutoTokenizer, AutoModelForImageTextToText
from qwen_vl_utils import process_vision_info

MODEL = "vankey/DocShield-9B"

tokenizer = AutoTokenizer.from_pretrained(MODEL, trust_remote_code=True)
processor = AutoProcessor.from_pretrained(MODEL, trust_remote_code=True)
model = AutoModelForImageTextToText.from_pretrained(
    MODEL, torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto"
)
model.eval()

SYSTEM_PROMPT = (
    "你是一个图像鉴伪专家，擅长结合视觉，文字结合伪造特征分析手段鉴别输入图像的真假。"
    "分析过程中，你会逐步分析，抽丝剥茧，找到图像伪造的蛛丝马迹，最终给出专业的鉴别结果及分析。"
)
USER_PROMPT = "请分析这张文档图片是否存在伪造或篡改风险，并输出一份专业、精炼、准确的防伪分析报告。"

messages = [
    {"role": "system", "content": [{"type": "text", "text": SYSTEM_PROMPT}]},
    {"role": "user", "content": [
        {"type": "image", "image": "image.jpg"},
        {"type": "text", "text": USER_PROMPT},
    ]},
]

text = processor.apply_chat_template(messages, tokenize=False,
                                     add_generation_prompt=True, enable_thinking=False)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(text=[text], images=image_inputs, videos=video_inputs,
                   padding=True, return_tensors="pt").to(model.device)

with torch.no_grad():
    out = model.generate(**inputs, max_new_tokens=1024, do_sample=False)

gen = [o[len(i):] for i, o in zip(inputs["input_ids"], out)]
print(processor.batch_decode(gen, skip_special_tokens=False)[0])

Inference notes

Image input — pass the image path directly in the message content; the processor handles resize/tokenization.
Thinking mode — --no-thinking (default) gives a direct report; --thinking enables Qwen3.5 chain-of-thought before the report (use a larger --max-new-tokens).
Decoding — greedy by default (do_sample=False); pass --do-sample with --temperature/--top-p/--top-k for sampling.
Precision — bfloat16 is the tested configuration (--dtype bf16).

Citation

bibtex
@article{docshield2026,
  title={DocShield: A Forensic Vision-Language Model for Document Forgery Analysis},
  author={DocShield},
  year={2026},
  url={https://arxiv.org/abs/2604.02694}
}

License

Apache-2.0.

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Model Details

Model Provider

vankey

Model Tree

Base

this model

Input Modalities

Text

Image

Video

Output Modalities