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+# Ming-Reasoning: Empowering MLLMs with Unified General and Spatial Reasoning
+
+📖 [Technical Report]() | 🤗 [Hugging Face](https://huggingface.co/inclusionAI/Ming-Reasoning)｜ 🤖 [ModelScope](https://www.modelscope.cn/models/inclusionAI/Ming-Reasoning)
+
+## Introduction
+
+We introduce Ming-Reasoning-7B, a model designed to excel in both general and spatial reasoning. Our approach integrates two key innovations: (1) a novel data pipeline that generates 294.2K high-quality data samples (168K for cold-start fine-tuning and 126.2K for RLVR), which feature logically coherent reasoning trajectories and have undergone comprehensive assessment; and (2) a dynamic multi-task training strategy with step-wise optimization to mitigate conflicts between data, and task-specific rewards for delivering tailored incentive signals. This combination of curated data and advanced training allows Ming-Reasoning-7B to set a new state-of-the-art (SOTA) across 8 benchmarks, showcasing superior performance in both general and spatial reasoning domains.
+![](assets/teaser.png)
+
+## 📌 Updates
+
+<!-- - [2025.07.08] 🔥 Our Technical Report is in public on arxiv. -->
+- [2025.07.07] 🔥 We release Ming-Reasoning 🤗 [Hugging Face](https://huggingface.co/inclusionAI/Ming-Reasoning) and 🤖 [ModelScope](https://www.modelscope.cn/models/inclusionAI/Ming-Reasoning).
+
+## Key Features
+
+- Unified Omni-Modality Perception: Ming-lite-omni, built on Ling, an MoE architecture LLM, resolves task conflicts and ensures coherent integration of tokens from different modalities through modality-specific routers.
+- Unified Perception and Generation: Ming-lite-omni achieves unified understanding and generation, enabling the model to interpret multimodal instructions and user intent during generation, which helps enhance generation quality and improves usability across multiple tasks.
+- Innovative Generation Capabilities: Ming-lite-omni can perceive all modalities and generate high-quality text, real-time speech, and vivid images simultaneously, delivering exceptional cross-modal performance across diverse tasks including image perception, audio-visual interaction, and image generation.
+
+## Evaluation
+
+We conduct a comprehensive evaluation of our models across two key domains: general and spatial
+reasoning. Our evaluation utilizes a diverse set of public benchmarks, grouped by the primary
+capability they measure:
+
+- General Reasoning (Mathematical & Logical): To evaluate this capability, we employ six benchmarks: MathVista, MathVision, MathVerse, DynaMath, WeMath, and LogicVista.
+
+|Models| MathVista| MathVision| MathVerse| DynaMath| WeMath| LogicVista| Avg. (Δ)|
+|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
+|***Base-Scale General Models***|
+|InternVL3-8B | 70.5| 30.0| 38.5| 25.7 |39.5 |44.5 |41.4|
+|InternVL3-9B | 69.0 | 29.3| 37.9 |25.1 |34.8| 49.0 |40.8|
+|Qwen2.5-VL-7B |68.1 |25.4 |41.1 |21.8 |36.2| 47.9| 40.1|
+|MUG-U-7B | 74.8 |26.1 |35.4 |17.2 |26.5 |39.8| 36.6|
+|SAIL-VL-1.6-8B | 74.2 |23.2| 33.4 |14.0 |29.6 |41.4| 36.0|
+|***Base-Scale Reasoning Models***|
+|WeThink-VL-7B| 71.6 |26.0| 44.2 |24.8 |**48.0** |**51.2**| 44.3 (+4.2)|
+|Taichu-VLR-7B | 72.3| 27.1 |46.7 |23.0 |44.0 |48.3 |43.6|
+|VLAA-Thinker-7B | 68.0 |26.4| **48.2** |22.4 |41.5 |48.5 |42.5 (+2.4)|
+|URSA-8B-PS-GRPO | 67.8 |**31.8** |41.5 |22.4| 38.3 |44.7 |41.1 (+8.2)|
+|Ovis2-8B |71.8 |25.9| 42.3 |20.4 |27.2 |39.4| 37.8|
+|***Our Models***|
+|Base Model |70.2| 25.9| 30.5| 20.2| 27.2| 37.8| 35.5|
+|Ming-Reasoning-CI-7B|  71.7| 29.2| 42.1| 25.0 |42.8| 46.8 |42.9 (+7.4)|
+|Ming-Reasoning-7B | **75.0** |31.5| 44.7 |**26.8** |41.8 |50.0 |**45.0 (+9.5)**|
+
+- Spatial Reasoning: We assess this skill using 2 benchmarks: CV-Bench and VSI-Bench
+    - CV-Bench:
+
+    | Models | Count | Relation | Depth | Distance | Avg. |
+    | :--- | :---: | :---: | :---: | :---: | :---: |
+    | ***Large-Scale Models*** | | | | | |
+    | GPT-4O | 65.9 | 85.7 | 87.8 | 78.2 | 78.9 |
+    | Gemini-1.5-pro | 70.4 | 85.2 | 82.4 | 72.8 | 77.4 |
+    | ***Base-Scale Models*** | | | | | |
+    | InternVL3-8B| **74.0** |  90.6  |  84.3  |  81.0  |  82.0  |
+    | Qwen2.5-VL-7B-Instruct | 65.2 |  86.6  | 70.6 | 79.8 | 75.0 |
+    | LLava-NEXT-Video-7B  | 59.3 | 77.0 | 71.3 | 54.7 | 65.2 |
+    | ***Our Models*** | | | | | |
+    | Ming-Reasoning-7B |  66.6  | **92.8** | **89.3** | **84.3** | **82.3** |
+
+    - VSI-Bench:
+
+    | | OC | AD| OS|RS |RDs |RDr |RP |AO |Avg. |
+    | :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
+    | ***Large-Scale Models*** | | | | | | | | | |
+    | Gemini-1.5-pro  | 56.2 | 30.9 | 64.1 | 43.6 | 51.3 | 46.3 | 36.0 | 34.6 | 45.4 |
+    | GPT-4O  | 46.2 | 5.3 | 43.8 | 38.2 | 37.0 | 41.3 | 31.5 | 28.5 | 34.0 |
+    | ***Base-Scale Models*** | | | | | | | | | |
+    | InternVL3-8B | **68.1** | **39.0** | 48.4 | 33.6 | **48.3** | 36.4 | 27.3 | **35.4** |  42.1  |
+    | Video-R1-7B | - | - | - | - | - | - | - | - | 37.1 |
+    | Qwen2.5-VL-7B-Instruct| 37.7 | 20.1 | 49.7  |  37.4  | 38.5 | 40.4 |  31.4  |  32.0  | 35.9 |
+    | LLava-NeXT-Video-7B|  48.5  | 14.0 |  47.8  | 24.2 |  43.5  | 42.4 |  **34.0**  | 30.6 | 35.6 |
+    | ***Our Models*** | | | | | | | | | |
+    | Ming-Reasoning-7B | 41.0 |  34.0  | **60.9** | **55.4** | 40.7 | **47.3** | 29.9 | 28.8 | **42.3** |
+
+## Installation
+
+Please download our model following Model Downloads, then you can refer to the following codes to run Ming-Reasoning model.
+The basic environment is `python=3.10`, `torch=2.6.0+cu124`, `transformers=4.49.0`
+## Example Usage
+
+We provide a small example on the usage of this repo. For detailed usage.
+
+``` python
+import os
+import torch
+
+from transformers import (
+    AutoProcessor,
+    AutoTokenizer,
+)
+
+import warnings
+import argparse
+from modeling_bailing_qwen2_5 import Bailing_qwen2_5NativeForConditionalGeneration
+from processing_bailing_qwen2_5 import Bailing_qwen2_5Processor
+
+warnings.filterwarnings("ignore")
+
+class BailingMMInfer:
+    def __init__(self,
+        model_name_or_path,
+        device="cuda",
+        max_pixels=None,
+        min_pixels=None,
+        video_max_pixels=768 * 28 * 28,
+        video_min_pixels=128 * 28 * 28,
+        generation_config=None
+    ):
+        super().__init__()
+        self.model_name_or_path = model_name_or_path
+
+        self.device = device
+
+        self.device_map = device
+
+        self.video_max_pixels = video_max_pixels if video_max_pixels is not None else 768 * 28 * 28
+        self.video_min_pixels = video_min_pixels if video_min_pixels is not None else 128 * 28 * 28
+
+        self.model, self.tokenizer, self.processor = self.load_model_processor()
+        if max_pixels is not None:
+            self.processor.max_pixels = max_pixels
+        if min_pixels is not None:
+            self.processor.min_pixels = min_pixels
+        if generation_config is None:
+            generation_config = {
+                "num_beams": 1,
+                "do_sample": True,
+                "temperature": 0.9
+            }
+
+        self.generation_config = generation_config
+
+
+    def load_model_processor(self):
+        
+        model = Bailing_qwen2_5NativeForConditionalGeneration.from_pretrained(
+            self.model_name_or_path,
+            torch_dtype=torch.bfloat16,
+            device_map=self.device_map,
+            _attn_implementation="flash_attention_2"
+        ).eval()
+
+        tokenizer = AutoTokenizer.from_pretrained(self.model_name_or_path, add_bos_token=True, trust_remote_code=True)
+        processor = Bailing_qwen2_5Processor.from_pretrained(self.model_name_or_path, trust_remote_code=True)
+
+        return model, tokenizer, processor
+
+    def generate(self, messages, max_new_tokens=512):
+        text = self.processor.apply_chat_template(
+            messages, tokenize=False, add_generation_prompt=True, use_system=True
+        )
+
+        image_inputs, video_inputs = self.processor.process_vision_info(messages)
+
+
+        inputs = self.processor(
+            text=[text],
+            images=image_inputs,
+            videos=video_inputs,
+            return_tensors="pt",
+        )
+        # print(inputs)
+        print(self.tokenizer.decode(inputs['input_ids'][0]))
+
+        inputs = inputs.to(self.device)
+
+        for k in inputs.keys():
+            if k == "pixel_values" or k == "pixel_values_videos":
+                inputs[k] = inputs[k].to(dtype=torch.bfloat16)
+
+        with torch.no_grad():
+            generated_ids = self.model.generate(
+                inputs,
+                max_new_tokens=max_new_tokens,
+                eos_token_id=self.processor.tokenizer.eos_token_id,
+                **self.generation_config,
+            )
+
+        generated_ids_trimmed = [
+            out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+        ]
+
+        output_text = self.processor.batch_decode(
+            generated_ids_trimmed, skip_special_tokens=False, clean_up_tokenization_spaces=False
+        )[0]
+
+        return output_text
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--model_name_or_path', type=str, default="inclusionAI/Ming-Reasoning")
+    parser.add_argument('--max_pixels', type=int, default=401408)
+    parser.add_argument('--min_pixels', type=int, default=401408)
+    parser.add_argument('--max_new_tokens', type=int, default=4096)
+
+    args = parser.parse_args()
+
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    # model_name_or_path = os.path.join(args.input_dir, args.model_name_or_path)
+    bailing2 = BailingMMInfer(
+        args.model_name_or_path, 
+        device=device, 
+        max_pixels=args.max_pixels, 
+        min_pixels=args.min_pixels
+    )
+
+    messages = [
+        {
+            "role": "system", 
+            "content": [
+                {"type": "text", "text": "You are a helpful assistant. When the user asks a question, your response must include two parts: first, the reasoning process enclosed in <think>...</think> tags, then the final answer enclosed in <answer>...</answer> tags. The critical answer or key result should be placed within \\boxed{}."}]},
+        {
+            "role": "user",
+            "content": [
+                {"type": "image", "image": "./assets/example1.png"},
+                {"type": "text", "text": "\nQuestion:\n\nRhombus $QRST$ has an area of 137.9 square meters. If $RT$ is 12.2 meters, find $QS$.\nA. 11.3\nB. 22.4\nC. 22.6\nD. 25.6"},
+            ],
+        },
+    ]
+    output_text = bailing2.generate(messages, max_new_tokens=args.max_new_tokens)
+    print(output_text)
+
+
+
+'''
+[Output]:
+
+<think>
+To find the length of \( QS \) in the rhombus \( QRST \), we can use the formula for the area of a rhombus, which is given by:
+
+\[
+\text{Area} = \frac{1}{2} \times d_1 \times d_2
+\]
+
+where \( d_1 \) and \( d_2 \) are the lengths of the diagonals. In this problem, we are given:
+- The area of the rhombus is 137.9 square meters.
+- One of the diagonals, \( RT \), is 12.2 meters.
+
+We need to find the length of the other diagonal, \( QS \).
+
+Let's denote:
+- \( d_1 = RT = 12.2 \) meters
+- \( d_2 = QS \)
+
+Substitute the known values into the area formula:
+
+\[
+137.9 = \frac{1}{2} \times 12.2 \times QS
+\]
+
+To solve for \( QS \), first multiply both sides by 2 to eliminate the fraction:
+
+\[
+275.8 = 12.2 \times QS
+\]
+
+Next, divide both sides by 12.2:
+
+\[
+QS = \frac{275.8}{12.2}
+\]
+
+Now, perform the division:
+
+\[
+QS \approx 22.6
+\]
+
+So, the length of \( QS \) is approximately 22.6 meters.
+
+Looking at the options provided:
+A. 11.3
+B. 22.4
+C. 22.6
+D. 25.6
+
+The correct answer is C. 22.6.
+</think>
+<answer>
+\boxed{C. 22.6}
+</answer><|im_end|>
+'''
+```
+
+## License and Legal Disclaimer
+
+This code repository is licensed under the MIT License, and the Legal Disclaimer is located in the LEGAL.md file under the project's root directory.
+
+## Citation
+
+If you find our work helpful, feel free to give us a cite.
+
+```
+@misc{Mingreasoning2025,
+      title  = {Ming-Reasoning: Empowering MLLMs with Unified General and Spatial Reasoning}, 
+      author = {Inclusion AI},
+      year = {2025},
+      archivePrefix = {arXiv},
+}
+```