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--- |
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license: mit |
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datasets: |
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- open-r1/OpenR1-Math-220k |
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--- |
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<div align='center'> |
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<img src='https://cdn-uploads.huggingface.co/production/uploads/61ebf3544fae7b86fdadb923/jgpDMnjUO-YrU6fnDawAr.png' style="border-radius: 15px;" /> |
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<h1>Namo R1</h1> |
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</div> |
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<p align="center"> |
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🤗 <a href="https://huggingface.co/lucasjin/Namo-500M-V1">Namo-500M-V1</a>   |   🐝 <a href="https://github.com/lucasjinreal/Namo-R1/issues/new">Community</a> |
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</p |
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> **You**: *I don't have GPUs to run VLMs.* **Namo R1:** Hold my beer.... let's do this on CPU. |
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**Namo R1** 🔥🔥 surpassed SmolVLM and Moondream2 in terms of same size! And we are keep evolving, more advanced models are under training! |
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## Introduction |
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We are excited to open-source **Namo**, an extremly small yet mighty MLLM. While numerous MLLMs exist, few offer true extensibility or fully open-source their training data, model architectures, and training schedulers - critical components for reproducible AI research. |
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The AI community has largely overlooked the potential of compact MLLMs, despite their demonstrated efficiency advantages. Our analysis reveals significant untapped potential in sub-billion parameter models, particularly for edge deployment and specialized applications. To address this gap, we're releasing Namo R1, a foundational 500M parameter model trained from scratch using innovative architectural choices. |
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Key innovations include: |
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1. **CPU friendly:** Even on CPUs, Namo R1 can runs very fast; |
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2. **Omni-modal Scalability:** Native support for future expansion into audio (ASR/TTS) and cross-modal fusion; |
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3. **Training Transparency:** Full disclosure of data curation processes and dynamic curriculum scheduling techniques. |
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👇 Video Demo Runs on **CPU**: |
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<video src='https://github.com/user-attachments/assets/eb353124-509e-4b87-8a0d-b0b37b5efba2 |
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'></video> |
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Github: https://github.com/lucasjinreal/Namo-R1/ |
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## Updates |
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- **`2025.02.21`**: more to come...! |
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- **`2025.02.21`**: 🔥🔥 The first version is ready to open, fire the MLLM power able to runs on CPU! |
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- **`2025.02.17`**: Namo R1 start training. |
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## Results |
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the result might keep updating as new models trained. |
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| Model | MMB-EN-T | MMB-CN-T | Size | |
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| -------------------- | -------------- | -------------- | ---- | |
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| Namo-500M | **68.8** | **48.7** | 500M | |
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| Namo-700M | training | training | 700M | |
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| Namo-500M-R1 | training | training | 500M | |
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| Namo-700M-R1 | training | training | 700M | |
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| SmolVLM-500M | 53.8 | 35.4 | 500M | |
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| SmolVLM-Instruct-DPO | 67.5 | 49.8 | 2.3B | |
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| Moondream1 | 62.3 | 19.8 | 1.9B | |
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| Moondream2 | 70 | 28.7 | 1.9B | |
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⚠️ Currently, the testing has only been conducted on a limited number of benchmarks. In the near future, more metrics will be reported. Even so, we've observed significant improvements compared to other small models. |
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## Get Started |
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#### Install & Run in Cli |
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All you need to do is: |
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```shell |
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pip install -U namo |
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``` |
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A simple demo would be: |
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```python |
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from namo.api.vl import VLInfer |
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# model will download automatically |
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model = VLInfer(model_type='namo') |
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# default will have streaming |
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model.generate('what is this?', 'images/cats.jpg', stream=True) |
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``` |
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That's all! |
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For cli multi-turn chat in terminal you can run `python demo.py`. (Namo cli directly in your terminal would be avaiable later.) |
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#### OpenAI server & Run in OpenWebUI |
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```shell |
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namo server --model checkpoints/Namo-500M-V1 |
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``` |
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then, you will have OpenAI like serving in local. |
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## Features of Namo R1 |
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In contrast to open-source VLMs like Qwen2.5-3B and MiniCPM, the Namo series offers the following features that enable anyone to train their own VLMs from scratch: |
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- **Extremely Small**: Our first series has only 500 million parameters yet powerful on various tasks. |
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- **OCR Capability**: With just a 500M model, you can perform multilingual OCR, covering not only Chinese and English but also Japanese and other languages. |
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- **Dynamic Resolution**: We support native dynamic resolution as input, making it robust for images of **any ratio**. |
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- **Fully Open Source**: We opensource all model codes including training steps and scripts! |
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- **R1 Support**: Yes, we now support R1 for post-training. |
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Above all, we are also ready to help when u want train your MLLM from scratch at any tasks! |
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## Roadmap |
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We are still actively training on new models, here are few things we will arrive: |
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- Speech model; |
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- Vision model with more decent vision encoders, such as SigLip2; |
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- TTS ability; |
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- Slightly larger models, up to 7B; |
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## Trouble Shooting |
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1. Got error when using deepspeed: ` AssertionError: no_sync context manager is incompatible with gradient partitioning logic of ZeRO stage 2` ? |
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Please upgrade transformers to 4.48+ and use latest deepspeed. |
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## Copyright |
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All right reserved by Namo authors, code released under MIT License. |
