tencent/WeVisDoc-4B

WeVisDoc

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WeVisDoc is an end-to-end document parser for page images. Fine-tuned from Qwen3-VL-2B-Instruct and Qwen3-VL-4B-Instruct, it turns a page into structured Markdown, with LaTeX formulas and HTML tables.

WeVisDoc-4B achieves an Overall score of 95.38 on OmniDocBench v1.6 and a mean Overall score of 75.54 across the three PureDocBench tracks, ranking first among the compared end-to-end parsers in all four settings.

WeVisDoc-4B leads the compared end-to-end parsers across all four reported settings. Bars show scores on OmniDocBench v1.6 and PureDocBench Clean, Digital, and Real.

Evaluation

The following tables include end-to-end document parsing specialists only. WeVisDoc results are means over three inference runs.

OmniDocBench v1.6

Model	Params	Overall ↑	TextEdit ↓	FormulaCDM ↑	TableTEDS ↑	TableTEDS_S ↑	ROEdit ↓
Nanonets-OCR2*	3B	83.20	0.108	80.35	80.10	85.26	0.211
OCRFlux-3B*	3B	83.31	0.126	88.75	73.78	77.98	0.217
POINTS-Reader	3B	83.37	0.096	85.72	73.98	77.40	0.198
Nanonets-OCR-s	3B	83.61	0.108	81.46	80.18	84.51	0.213
olmOCR-2-7B*	7B	85.51	0.106	88.84	78.32	82.81	0.223
olmOCR	7B	85.74	0.139	88.10	83.00	87.17	0.216
DeepSeek-OCR*	3B	86.31	0.077	84.71	81.87	86.07	0.171
OCRVerse	4B	88.60	0.063	89.61	82.44	86.27	0.163
UniRec-0.1B*	0.1B	88.91	0.088	92.14	83.40	86.79	0.146
DeepSeek-OCR 2	3B	90.25	0.050	91.84	83.89	87.75	0.144
dots.ocr	3B	90.77	0.048	89.95	87.18	90.58	0.138
FD-RL*	4B	91.21	0.055	92.92	86.22	90.92	0.145
HunyuanOCR	1B	92.03	0.048	88.60	92.37	93.99	0.138
dots.mocr*	3B	92.57	0.042	92.09	89.78	92.92	0.133
FireRed-OCR	2B	93.26	0.037	95.44	88.04	91.06	0.131
Logics-Parsing-v2	4B	93.33	0.041	95.65	88.42	91.98	0.137
Qianfan-OCR	4B	93.90	0.040	95.08	90.53	93.31	0.130
Unlimited-OCR	3B-A0.5B	93.92	0.042	95.79	90.16	93.32	0.129
HunyuanOCR-1.5	1B	94.74	0.039	94.50	93.67	94.71	0.129
WeVisDoc-2B	2B	95.06	0.038	95.94	93.03	95.26	0.130
WeVisDoc-4B	4B	95.38	0.036	96.81	92.95	95.34	0.125

PureDocBench

Model	Params	Avg₃ ↑	Clean Overall ↑	Digital Degraded Overall ↑	Real Degraded Overall ↑
OCRFlux-3B	3B	42.06	47.14	41.82	37.21
DeepSeek-OCR	3B	46.98	53.50	46.95	40.48
UniRec-0.1B	0.1B	48.59	58.91	52.42	34.44
POINTS-Reader*	3B	49.24	53.78	51.24	42.69
DeepSeek-OCR-2	3B	49.51	55.53	49.41	43.60
Qianfan-OCR	4B	51.04	57.22	50.85	45.06
olmOCR-7B	7B	55.90	62.56	57.84	47.30
Nanonets-OCR2	3B	58.36	64.83	61.23	49.03
HunyuanOCR	1B	60.56	65.61	61.49	54.58
Unlimited-OCR*	3B-A0.5B	62.76	71.28	63.62	53.39
olmOCR-2-7B	7B	63.78	69.36	65.87	56.10
dots.ocr	3B	64.55	72.01	65.95	55.68
Nanonets-OCR-s*	3B	65.37	71.26	66.56	58.28
FireRed-OCR	2B	65.57	70.81	68.49	57.42
HunyuanOCR-1.5*	1B	68.79	73.98	70.81	61.59
OCRVerse	4B	69.40	73.18	71.36	63.66
dots.mocr	3B	70.39	76.27	73.16	61.73
Logics-Parsing-v2	4B	72.61	76.35	73.85	67.64
FD-RL	4B	73.92	78.38	76.33	67.04
WeVisDoc-2B	2B	73.86	79.36	76.62	65.60
WeVisDoc-4B	4B	75.54	79.81	77.74	69.08

Avg₃ is the mean of the three PureDocBench track-level Overall scores. * marks baseline results obtained with our evaluation pipeline; unmarked baseline results are taken from the corresponding papers.

Quick start

Python 3.10+ is required. Install the vLLM and client dependencies:

python -m venv .venv
source .venv/bin/activate
python -m pip install -r requirements-vllm.txt

Start the service in the first terminal:

bash scripts/serve_vllm.sh Tencent/WeVisDoc-2B

Use Tencent/WeVisDoc-4B instead to run the 4B version.

Then process all bundled images from a second terminal:

source .venv/bin/activate
bash scripts/run_demo.sh

Predictions are written to outputs/predictions/.

Serve with vLLM

python -m venv .venv
source .venv/bin/activate
python -m pip install -r requirements-vllm.txt
bash scripts/serve_vllm.sh Tencent/WeVisDoc-2B

Replace the model ID with Tencent/WeVisDoc-4B to serve the 4B version. The launcher requires vLLM >=0.11.1. Extra arguments are passed to vLLM. To expose the service on the network, set HOST=0.0.0.0. For two GPUs and a larger context:

CUDA_VISIBLE_DEVICES=0,1 TENSOR_PARALLEL_SIZE=2 MAX_MODEL_LEN=65536 \
  bash scripts/serve_vllm.sh Tencent/WeVisDoc-4B --dtype bfloat16
curl --fail http://127.0.0.1:8000/health

Environment variable	Default	Meaning
WEVISDOC_MODEL_PATH / MODEL_PATH	Unset	Model ID or checkpoint; positional argument takes precedence, then WEVISDOC_MODEL_PATH
SERVED_MODEL_NAME	wevisdoc	API model alias; also read by the client
HOST / PORT	127.0.0.1 / 8000	Listening address
TENSOR_PARALLEL_SIZE	1	Number of tensor-parallel GPUs
MAX_MODEL_LEN	32768	Total context budget: text, image and output tokens
GPU_MEMORY_UTILIZATION	0.9	GPU memory fraction
MAX_NUM_SEQS	8	Maximum concurrent sequences
OMP_NUM_THREADS	1	CPU preprocessing threads
VLLM_API_KEY	Unset	Optional server authentication, handled by vLLM

Use a separate virtual environment from Transformers to avoid conflicting PyTorch packages.

Call the service

A client machine only needs python -m pip install -r requirements.txt. The examples expect PNG, JPEG, or WebP page images:

python -m wevisdoc.client --image page.png --output results/page.md
python -m wevisdoc.client --image-dir images --result-dir results --workers 4

--image-dir processes images in that directory (not recursively) and writes one Markdown file per image, such as results/page.png.md. Existing nonempty results are skipped unless --overwrite is set.

To process every bundled image in demos/inputs/:

OPENAI_BASE_URL=http://127.0.0.1:8000/v1 \
  bash scripts/run_demo.sh --workers 4

The demo writes one Markdown file per image to outputs/predictions/.

The client reads OPENAI_BASE_URL (default http://127.0.0.1:8000/v1), OPENAI_API_KEY (default EMPTY), and SERVED_MODEL_NAME. Match OPENAI_API_KEY to VLLM_API_KEY when authentication is enabled.

Override defaults with --base-url, --model, --timeout (600 seconds), --temperature (0), or --max-tokens (8192). Increase the token or context budget if output is truncated; reduce image size, context, or concurrency if GPU memory is insufficient.

Local Transformers inference

Use a separate environment from vLLM:

python -m pip install -r requirements-local.txt
python -m wevisdoc.local --model Tencent/WeVisDoc-2B \
  --image page.png --output results/page.md

Use Tencent/WeVisDoc-4B for the 4B version. --model can be omitted when WEVISDOC_MODEL_PATH is set. Local inference supports --device-map (default auto) and --max-tokens (8192). Render PDFs to page images first.