| --- |
| license: apache-2.0 |
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| |
| # 🧠 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. |
|
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| Built independently with a focus on Indian academic excellence 🇮🇳 |
|
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| --- |
|
|
| ## 🚀 Overview |
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|
| - **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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| --- |
|
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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: |
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| **GPT-OSS-20B** |
| - AIME 2024 (with tools): ~96.0% |
| - AIME 2025 (with tools): ~98.7% |
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| --- |
|
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| # 📊 Benchmark Results |
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| --- |
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| ## 🧠 STEM & Puzzle (Multimodal) |
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|
| | Benchmark | Manthan-M1 | |
| |------------|------------| |
| | We-Math | **83.5** | |
| | DynaMath | **84.9** | |
| | ZEROBench | **8** | |
| | ZEROBench_sub | **34.8** | |
| | BabyVision | **38.0** | |
| |
| --- |
| |
| ## 🌍 General VQA |
| |
| | Benchmark | Score | |
| |------------|--------| |
| | RealWorldQA | **82.5** | |
| | MMStar | **81.2** | |
| | HallusionBench | **69.0** | |
| | MMBenchEN-DEV-v1.1 | **92.0** | |
| | SimpleVQA | **69.0** | |
| |
| --- |
| |
| ## 📄 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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| --- |
|
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| ## 🎥 Video Understanding |
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|
| | 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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| --- |
|
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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 |
|
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| | 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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| --- |
|
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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** | |
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| 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 |
|
|
| --- |
|
|
|
|
|
|
| # 🇮🇳 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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| --- |
|
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| # 📜 License |
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| Apache 2.0 |
|
|
| --- |
|
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| # 👤 Author |
|
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| Built independently by an Indian developer focused on competitive reasoning systems. |
|
|
| --- |
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| **Manthan-M1 — Churning Intelligence.** |
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|
| ``` |
| ``` |
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|
