🤗 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: 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**: 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.