README.md

---
license: mit
datasets:
- open-r1/OpenR1-Math-220k
---
<div align='center'>
<img src='https://cdn-uploads.huggingface.co/production/uploads/61ebf3544fae7b86fdadb923/jgpDMnjUO-YrU6fnDawAr.png' style="border-radius: 15px;" />
<h1>Namo R1</h1>
</div>

<p align="center">
          🤗 <a href="https://huggingface.co/lucasjin/Namo-500M-V1">Namo-500M-V1</a>&nbsp&nbsp | &nbsp&nbsp🐝 <a href="https://github.com/lucasjinreal/Namo-R1/issues/new">Community</a>
</p

> **You**: *I don't have GPUs to run VLMs.*  **Namo R1:** Hold my beer.... let's do this on CPU.

**Namo R1** 🔥🔥 surpassed SmolVLM and Moondream2 in terms of same size! And we are keep evolving, more advanced models are under training!

## Introduction

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.

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.

Key innovations include:

1. **CPU friendly:** Even on CPUs, Namo R1 can runs very fast;
2. **Omni-modal Scalability:** Native support for future expansion into audio (ASR/TTS) and cross-modal fusion;
3. **Training Transparency:** Full disclosure of data curation processes and dynamic curriculum scheduling techniques.

👇 Video Demo Runs on **CPU**:

<video src='https://github.com/user-attachments/assets/eb353124-509e-4b87-8a0d-b0b37b5efba2
'></video>

Github: https://github.com/lucasjinreal/Namo-R1/

## Updates

- **`2025.02.21`**: more to come...!
- **`2025.02.21`**: 🔥🔥 The first version is ready to open, fire the MLLM power able to runs on CPU!
- **`2025.02.17`**: Namo R1 start training.

## Results

the result might keep updating as new models trained.

| Model                | MMB-EN-T       | MMB-CN-T       | Size |
| -------------------- | -------------- | -------------- | ---- |
| Namo-500M            | **68.8** | **48.7** | 500M |
| Namo-700M            | training       | training       | 700M |
| Namo-500M-R1         | training       | training       | 500M |
| Namo-700M-R1         | training       | training       | 700M |
| SmolVLM-500M         | 53.8           | 35.4           | 500M |
| SmolVLM-Instruct-DPO | 67.5           | 49.8           | 2.3B |
| Moondream1           | 62.3           | 19.8           | 1.9B |
| Moondream2           | 70             | 28.7           | 1.9B |

⚠️ 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.

## Get Started

#### Install & Run in Cli

All you need to do is:

```shell
pip install -U namo
```

A simple demo would be:

```python
from namo.api.vl import VLInfer

# model will download automatically
model = VLInfer(model_type='namo')

# default will have streaming
model.generate('what is this?', 'images/cats.jpg', stream=True)
```

That's all!

For cli multi-turn chat in terminal you can run `python demo.py`. (Namo cli directly in your terminal would be avaiable later.)

#### OpenAI server & Run in OpenWebUI

```shell
namo server --model checkpoints/Namo-500M-V1
```

then, you will have OpenAI like serving in local.



## Features of Namo R1

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:

- **Extremely Small**: Our first series has only 500 million parameters yet powerful on various tasks.
- **OCR Capability**: With just a 500M model, you can perform multilingual OCR, covering not only Chinese and English but also Japanese and other languages.
- **Dynamic Resolution**: We support native dynamic resolution as input, making it robust for images of **any ratio**.
- **Fully Open Source**: We opensource all model codes including training steps and scripts!
- **R1 Support**: Yes, we now support R1 for post-training.

Above all, we are also ready to help when u want train your MLLM from scratch at any tasks!

## Roadmap

We are still actively training on new models, here are few things we will arrive:

- Speech model;
- Vision model with more decent vision encoders, such as SigLip2;
- TTS ability;
- Slightly larger models, up to 7B;

## Trouble Shooting

1. Got error when using deepspeed: ` AssertionError: no_sync context manager is incompatible with gradient partitioning logic of ZeRO stage 2` ?

Please upgrade transformers to 4.48+ and use latest deepspeed.

## Copyright

All right reserved by Namo authors, code released under MIT License.