maifoundations/DualMindVLM

Model Sources

• Repository: https://github.com/maifoundations/DualMindVLM
• Paper: https://arxiv.org/pdf/2511.16670

Quick Start

The model is fine-tuned from Qwen2.5-VL-7B-Instruct. We provide an inference example using the Transformers inference backend.

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

model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    "maifoundations/DualMindVLM",
    torch_dtype=torch.bfloat16,
    attn_implementation="flash_attention_2",
    device_map="auto",
)

# default processer
processor = AutoProcessor.from_pretrained("maifoundations/DualMindVLM")

SYSTEM_PROMPT = """You are a Vision-Language Model answering questions about images. 
Follow these rules strictly:  
1. Judge the length of reasoning needed.
- Short: start with "Short Thinking:".
- Long: start with "Long Thinking:".
2. Short Thinking: give a concise thinking process which is sufficient to answer the question, then provide the final answer.
3. Long Thinking: give a structured reasoning process of the question and the image, including question analysis, visual details description, self-verification and then provide the final answer.
4. The final answer MUST BE put in \\boxed{}."""

messages = [
    {"role": "system", "content": [{"type": "text", "text": SYSTEM_PROMPT}]},
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": image_path,
            },
            {"type": "text", "text": question},
        ],
    }
]

# Preparation for inference
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")

# Inference: Generation of the output
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)

Citation

@article{lin2025dualmindvlm,
  title     = {Learning to Think Fast and Slow for Visual Language Models},
  author    = {Chenyu Lin and Cheng Chi and Jinlin Wu and Sharon Li and Kaiyang Zhou},
  journal   = {arXiv preprint arXiv:2511.16670},
  year      = {2025}
}