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stockmark/
Stockmark-DocReasoner-Qwen2.5-VL-32B

Name: Stockmark-DocReasoner-Qwen2.5-VL-32B
Availability: InStock

Qwen/Qwen2.5-VL-32B-Instruct

finetune

apache-2.0

text-generation-inference

endpoints_compatible

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Stockmark-DocReasoner-Qwen2.5-VL-32B

Introduction

Stockmark-DocReasoner-Qwen2.5-VL-32B is a vision-language model specialized for Japanese document understanding and reasoning, particularly in the manufacturing domain.

Built on top of Qwen2.5-VL-32B-Instruct, the model is further trained to acquire:

Explicit Chain-of-Thought (CoT) reasoning ("thinking") capability
Multi-modal understanding across documents, charts, tables, and diagrams
Domain-specific knowledge for manufacturing and business documents

It is designed to extract implicit knowledge from visually rich and structurally complex documents such as:

Technical documentation
Engineering design drawing
Experimental reports
Business documents

This project is supported by GENIAC.

Evaluation

We evaluated Japanese document understanding performance using the following three benchmarks:

All benchmark evaluation were performed using llm-jp-eval-mm and adopted the LLM-as-a-judge score as the comparison metric (using gpt-4o-2024-11-20 as judge model). Additionally, given the practical requirements for answer accuracy in business-domain VQA, we employed a binary scoring criterion when evaluating JA-Business-Doc-RQ-Bench and BusinessSlideVQA, and redesigned a prompt incorporating specific requirements (please refer to JA-Business-Doc-RQ-Bench for details).

JA-Business-Doc-RQ-Bench

Model	Overall	Answer Types	Image Types
Yes/No	Factoid	Numerical	Chart	Document	Table	Diagram
gpt-5.2-2025-12-11 (reasoning high)	95.20	93.22	96.55	95.54	98.25	96.61	94.64	91.23
Qwen3-VL-32B-Thinking	94.32	89.83	100	93.75	100	96.61	96.43	84.21
Stockmark-DocReasoner-Qwen2.5-VL-32B	85.15	88.14	87.93	82.14	78.95	96.61	82.14	82.46
Qwen3-VL-32B-Instruct	83.84	69.49	96.55	84.82	87.72	88.14	78.57	80.70
Qwen2.5-VL-32B-Instruct	79.04	72.88	81.03	81.25	82.46	86.44	67.86	78.95
gpt-4o-2024-11-20	59.39	67.80	51.72	58.93	56.14	62.71	55.36	63.16

JDocQA

Model	LLM	Acc
Stockmark-DocReasoner-Qwen2.5-VL-32B	4.0	0.31
Qwen3-VL-32B-Thinking	4.0	0.29
Qwen3-VL-32B-Instruct	4.0	0.25
Qwen2.5-VL-32B-Instruct	3.6	0.25
gpt-4o-2024-11-20	3.6	0.22

BusinessSlideVQA

Model	Acc
Stockmark-DocReasoner-Qwen2.5-VL-32B	77.27
Qwen3-VL-32B-Thinking	85.91
Qwen3-VL-32B-Instruct	82.27
Qwen2.5-VL-32B-Instruct	68.64
gpt-4o-2024-11-20	63.18

Quickstart

Inference using 🤗Transformers

Please make sure to have transformers>=4.49.0 installed.

pip install transformers>=4.49.0 accelerate torchvision qwen-vl-utils flash-attn

The following is a code snippet demonstrating how to use Stockmark-DocReasoner-Qwen2.5-VL-32B in pure transformers.

python

from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info
import torch

model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    "stockmark/Stockmark-DocReasoner-Qwen2.5-VL-32B",
    torch_dtype=torch.bfloat16,
    attn_implementation="flash_attention_2",
    device_map="auto",
)

processor = AutoProcessor.from_pretrained("stockmark/Stockmark-DocReasoner-Qwen2.5-VL-32B")

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": "assets/demo.png",
            },
            {"type": "text", "text": "30歳未満の社員に対するアンケート回答結果で、最も割合が高かった「使用頻度」は何ですか？"},
        ],
    }
]

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

generated_ids = model.generate(**inputs, max_new_tokens=1024)
generated_ids_trimmed = [
    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)

Inference using vLLM

The following is a code snippet demonstrating how to use Stockmark-DocReasoner-Qwen2.5-VL-32B in vLLM.

python

import os
from transformers import AutoProcessor
from qwen_vl_utils import process_vision_info
from vllm import LLM, SamplingParams

os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"

def main():
    
    llm = LLM(
        model="stockmark/Stockmark-DocReasoner-Qwen2.5-VL-32B",
        trust_remote_code=True,
        dtype="bfloat16",
    )
    processor = AutoProcessor.from_pretrained("stockmark/Stockmark-DocReasoner-Qwen2.5-VL-32B")
    message = [
        {
            "role": "user",
            "content": [
                {
                    "type": "image",
                    "image": "assets/demo.png",
                },
                {"type": "text", "text": "30歳未満の社員に対するアンケート回答結果で、最も割合が高かった「使用頻度」は何ですか？"},
            ],
        }
    ]
    texts = processor.apply_chat_template(
        message, tokenize=False, add_generation_prompt=True
    )
    image_inputs, video_inputs = process_vision_info(message)

    mm_data = {}
    if image_inputs is not None:
        mm_data["image"] = image_inputs
    if video_inputs is not None:
        mm_data["video"] = video_inputs
    
    inputs = {
        "prompt": texts,
        "multi_modal_data": mm_data,
    }

    sampling_params = SamplingParams(
        temperature=0,
        max_tokens=1024
    )

    outputs = llm.generate(
        inputs,
        sampling_params=sampling_params,
    )

    answer = outputs[0].outputs[0].text
    print(answer)

if __name__ == "__main__":
    main()

Output Format

Default Thinking Mode

Stockmark-DocReasoner-Qwen2.5-VL-32B outputs structured reasoning by default:

text

<think>
...reasoning process...
</think>
<answer>
...final answer...
</answer>

Special Inference Modes

In addition to default reasoning outputs, Stockmark-DocReasoner-Qwen2.5-VL-32B supports prompt-based task switching to enable fast and structured inference for downstream applications.

STMK HTML: Convert the input document into a structured HTML representation.
STMK Markdown: Convert documents into Markdown format.
STMK JSON: Extract document content into structured JSON.
STMK SMILES: Extract chemical structures from diagrams into SMILES format.

Developed by

Stockmark Inc.

Citation

bibtex

@misc{stockmark_docreasoner_2026,
  title={Stockmark-DocReasoner-Qwen2.5-VL-32B},
  author={Stockmark Inc.},
  year={2026}
}