array
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Qwen2.5-VL-Mull

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 ---
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 ---
 library_name: transformers
+license: apache-2.0
+base_model: Qwen/Qwen2.5-VL-7B-Instruct
+tags:
+  - multimodal
+  - vision-language
+  - spatial-reasoning
+  - latent-reasoning
+pipeline_tag: image-text-to-text
 ---
+# Mull-Tokens: Modality-Agnostic Latent Thinking
+This is the model for the paper **"Mull-Tokens: Modality-Agnostic Latent Thinking"**.
+[[Paper]](https://arxiv.org/abs/2512.10941) | [[Project Page]](https://arijitray1993.github.io/mulltokens/) | [[Code]](https://github.com/arijitray1993/mull)
+## Overview
+Mull-Tokens are latent tokens that can be pre-trained to hold intermediate information in either image or text modalities so as to think towards the correct answer. Across four challenging spatial reasoning benchmarks, Mull-Tokens achieve a **+3% average improvement** and up to **+16%** on reasoning-heavy splits compared to the strongest baseline.
+## Available Models
+| Model | Description |
+|---|---|
+| [array/Qwen2.5-VL-Mull](https://huggingface.co/array/Qwen2.5-VL-Mull) | Mull-Tokens with multimodal warm-up |
+| [array/Qwen2.5-VL-MullGRPO](https://huggingface.co/array/Qwen2.5-VL-MullGRPO) | Mull-Tokens + GRPO reinforcement learning |
+## Quick Start
+```python
+from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
+from qwen_vl_utils import process_vision_info
+import torch
+# Choose model: "array/Qwen2.5-VL-Mull" or "array/Qwen2.5-VL-MullGRPO"
+MODEL_ID = "array/Qwen2.5-VL-Mull"
+NUM_LATENTS = 20
+# Load model and processor
+model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    MODEL_ID,
+    torch_dtype=torch.bfloat16,
+    attn_implementation="flash_attention_2",
+    device_map="auto",
+)
+processor = AutoProcessor.from_pretrained(MODEL_ID)
+# Prepare your question
+image_path = "path/to/your/image.jpg"
+question = "If you stand at the X marked point and turn left, will the table be to your left or right? Please choose between the following answer choices: A. left. B. right. "
+question_type = "multiple choice"
+QUESTION_TEMPLATE_LATENT = (
+    "{Question}\n"
+    "Please think about this question deeply. "
+    "It's encouraged to include self-reflection or verification in the reasoning process. "
+    "Provide your final answer between the <answer> </answer> tags."
+)
+TYPE_TEMPLATE = {
+    "multiple choice": " Please provide only the single option letter (e.g., A, B, C, D, etc.) within the <answer> </answer> tags.",
+    "numerical": " Please provide the numerical value (e.g., 42 or 3.14) within the <answer> </answer> tags.",
+    "OCR": " Please transcribe text from the image/video clearly and provide your text answer within the <answer> </answer> tags.",
+    "free-form": " Please provide your text answer within the <answer> </answer> tags.",
+    "regression": " Please provide the numerical value (e.g., 42 or 3.14) within the <answer> </answer> tags.",
+}
+prompt = QUESTION_TEMPLATE_LATENT.format(Question=question) + TYPE_TEMPLATE[question_type]
+# Build messages with latent thinking tokens
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image", "image": image_path},
+            {"type": "text", "text": prompt},
+        ],
+    },
+    # IMPORTANT: Mull-Tokens requires latent thinking tokens before answer generation
+    {
+        "role": "assistant",
+        "content": [
+            {
+                "type": "text",
+                "text": "<think>" + "<|latent_pad|>" * NUM_LATENTS + "</think>\n",
+            }
+        ],
+    },
+]
+# Process inputs
+text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=False)
+text = text.replace("<|im_end|>\n", "")  # Remove end token so model continues generating
+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)
+# Generate response
+with torch.no_grad():
+    output_ids = model.generate(
+        **inputs,
+        max_new_tokens=512,
+        do_sample=False,
+    )
+# Decode output (skip input tokens)
+generated_ids = output_ids[:, inputs["input_ids"].shape[1]:]
+response = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(response)
+```
+## Citation
+```bibtex
+@misc{ray2025mulltokensmodalityagnosticlatentthinking,
+      title={Mull-Tokens: Modality-Agnostic Latent Thinking},
+      author={Arijit Ray and Ahmed Abdelkader and Chengzhi Mao and Bryan A. Plummer and Kate Saenko and Ranjay Krishna and Leonidas Guibas and Wen-Sheng Chu},
+      year={2025},
+      eprint={2512.10941},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2512.10941},
+}
+```