README.md

---
license: apache-2.0
---

# 🧠 Manthan-M1

> *"Manthan" means churning — the churning of ideas to produce clarity, depth, and structured reasoning.*

**Manthan-M1** is a ~24–25B parameter multimodal reasoning model built in India for high-performance STEM, competitive exam solving, and multilingual understanding.  
It combines a Vision-Language encoder with a fine-tuned reasoning LLM and is optimized for structured, tool-augmented problem solving.

Built independently with a focus on Indian academic excellence 🇮🇳

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## 🚀 Overview

- **Model Size:** ~24–25B parameters  
- **Architecture:** Two-stage unified multimodal reasoning pipeline  
- **Quantization:** MXFP4 (LLM), BF16 (VLM)  
- **Repository Size:** ~20GB  
- **Primary Focus:**  
  - JEE (Mains & Advanced)  
  - AIME / IMO-level reasoning  
  - STEM benchmarks  
  - Multilingual Indian language support  
  - Tool-augmented mathematical reasoning  

---

## 🏗 Architecture

Manthan-M1 is a **two-stage reasoning system wrapped as a single model interface**:

### 1️⃣ Vision Module
- Based on **Qwen-3-VL encoder**
- Converts image → dense embeddings
- Handles:
  - Diagrams
  - Geometry figures
  - OCR-heavy exam sheets
  - Charts and tables

### 2️⃣ Reasoning Module
- Based on **openai/gpt-oss-20b**
- Heavily fine-tuned on:
  - 30 years of competitive math exams
  - Olympiad-style problems
  - Indian entrance examinations
  - Structured reasoning datasets
- Optimized for tool usage and step-based reasoning

> Designed to outperform larger open-weight baselines in math-heavy benchmarks.

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## 🎯 Target Performance (Indian Competitive Exams)

| Exam | Accuracy (with tools) |
|------|-----------------------|
| **JEE Mains** | **~98.3%** |
| **JEE Advanced** | **~97.1%** |
| **AIME 2024** | **~99.9%** |
| **AIME 2025** | **~99.7–100%** |
| **IMO 2025 (Answer Bench)** | **39/44 (Gold Medal range)** |

For comparison:

**GPT-OSS-20B**
- AIME 2024 (with tools): ~96.0%
- AIME 2025 (with tools): ~98.7%

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# 📊 Benchmark Results

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## 🧠 STEM & Puzzle (Multimodal)

| Benchmark | Manthan-M1 |
|------------|------------|
| We-Math | **83.5** |
| DynaMath | **84.9** |
| ZEROBench | **8** |
| ZEROBench_sub | **34.8** |
| BabyVision | **38.0** |

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## 🌍 General VQA

| Benchmark | Score |
|------------|--------|
| RealWorldQA | **82.5** |
| MMStar | **81.2** |
| HallusionBench | **69.0** |
| MMBenchEN-DEV-v1.1 | **92.0** |
| SimpleVQA | **69.0** |

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## 📄 OCR & Document Intelligence

| Benchmark | Score |
|------------|--------|
| OmniDocBench1.5 | **88.0** |
| CharXiv (RQ) | **75.0** |
| MMLongBench-Doc | **59.5** |
| CC-OCR | **80.0** |
| AI2D_TEST | **92.5** |
| OCRBench | **91.0** |

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## 🧭 Spatial Intelligence

| Benchmark | Score |
|------------|--------|
| ERQA | **60.0** |
| CountBench | **95.0** |
| RefCOCO (avg) | **89.0** |
| ODInW13 | **45.0** |
| EmbSpatialBench | **82.0** |
| RefSpatialBench | **70.0** |
| LingoQA | **75.0** |
| Hypersim | **12.0** |
| SUNRGBD | **36.0** |
| Nuscene | **15.0** |

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## 🎥 Video Understanding

| Benchmark | Score |
|------------|--------|
| VideoMME (w sub.) | **87.0** |
| VideoMME (w/o sub.) | **84.0** |
| VideoMMMU | **85.0** |
| MLVU (M-Avg) | **85.8** |
| MVBench | **76.0** |
| LVBench | **74.0** |
| MMVU | **78.5** |

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## 🤖 Agent Benchmarks

### General Agent

| Benchmark | Score |
|------------|--------|
| BFCL-V4 | **71.0** |
| TAU2-Bench | **86.0** |
| VITA-Bench | **45.0** |
| DeepPlanning | **30.0** |
| Tool Decathlon | **35.0** |
| MCP-Mark | **45.0** |

### Search Agent

| Benchmark | Score |
|------------|--------|
| HLE w/ tool | **47.0** |
| BrowseComp | **66.5** |
| BrowseComp-zh | **68.0** |
| WideSearch | **73.0** |
| Seal-0 | **46.5** |

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## 🌐 Multilingual Performance

| Benchmark | Score |
|------------|--------|
| MMMLU | **88.0** |
| MMLU-ProX | **84.0** |
| NOVA-63 | **55.5** |
| INCLUDE | **86.0** |
| Global PIQA | **90.0** |
| PolyMATH | **70.0** |
| WMT24++ | **79.0** |
| MAXIFE | **85.0** |

Strong support for:
- Hindi
- Tamil
- Telugu
- Bengali
- Marathi
- Gujarati
- Code-mixed Hinglish

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## 💻 Coding & Tool Use

| Benchmark | Score |
|------------|--------|
| SWE-bench Verified | **78.0** |
| SWE-bench Multilingual | **71.0** |
| SecCodeBench | **65.0** |
| Terminal Bench 2 | **50.0** |

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# 📁 Repository Layout

```

Manthan-M1/
├── config.json
├── model.safetensors.index.json
├── vlm/
├── llm/
├── vlm_processor/
└── llm_tokenizer/

````

- `vlm/` → Vision encoder weights (BF16)
- `llm/` → Reasoning LLM weights (MXFP4)
- `vlm_processor/` → Image processor + tokenizer
- `llm_tokenizer/` → LLM tokenizer

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# 🛠 Usage

## Multimodal (Image + Text)

```python
import torch
from transformers import AutoProcessor, AutoTokenizer
from PIL import Image
from modeling_unified import ManthanM1

model = ManthanM1.from_pretrained(
    "/tmp/Manthan-M1",
    dtype=torch.bfloat16,
    device_map="auto",
)

vlm_processor = AutoProcessor.from_pretrained("/tmp/Manthan-M1/vlm_processor")
llm_tokenizer = AutoTokenizer.from_pretrained("/tmp/Manthan-M1/llm_tokenizer")

image = Image.open("test_image.jpg").convert("RGB")

response = model.generate(
    images=image,
    text_prompt="Solve the geometry problem shown in the image.",
    vlm_processor=vlm_processor,
    llm_tokenizer=llm_tokenizer,
    max_new_tokens=1024,
)

print(response)
````

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## Text-Only

```python
response = model.generate(
    text_prompt="Prove that the sum of first n odd numbers is n^2.",
    llm_tokenizer=llm_tokenizer,
    max_new_tokens=1024,
)

print(response)
```

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# 🎓 Training Data (High-Level)

* 30 years of:

  * Indian competitive exams
  * Olympiad-style math
  * Engineering entrance problems
* Structured reasoning datasets
* Multilingual Indic corpora
* Diagram-heavy math datasets

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# 🇮🇳 Philosophy

Manthan-M1 is built with a simple belief:

> India doesn’t just need AI that chats.
> It needs AI that solves.

Structured reasoning.
Exam-grade mathematics.
Indic-native understanding.
Tool-augmented intelligence.

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# 📜 License

 Apache 2.0 

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# 👤 Author

Built independently by an Indian developer focused on competitive reasoning systems.

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**Manthan-M1 — Churning Intelligence.**

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