Namo R1
🤗 Namo-500M-V1 | 🐝 Community
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:
- CPU friendly: Even on CPUs, Namo R1 can runs very fast;
- Omni-modal Scalability: Native support for future expansion into audio (ASR/TTS) and cross-modal fusion;
- Training Transparency: Full disclosure of data curation processes and dynamic curriculum scheduling techniques.
👇 Video Demo Runs on CPU:
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:
pip install -U namo
A simple demo would be:
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
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
- 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.
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