A unified training engine for multimodal models, supporting vision-language, diffusion, and MoE architectures with FSDP2, Muon, and Liger kernels.
EvolvingLMMs-Lab/NEO
Open-source native vision-language models (NEO series) that process pixels and words in one encoder-free dense architecture, with 2B and 9B releases.
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- Last commit
- 3 months ago
Key features
- 01Encoder-free dense architecture that unifies pixel and word encoding in one model.
- 022B and 9B model weights released across training stages.
- 03Evaluation guide and code built on VLMEvalKit.
- 04Training code for NEO models in a dedicated guide.
- 05Covers single-image, multi-image, video and spatial understanding tasks.
Use cases
- Researching native or encoder-free multimodal model design.
- Benchmarking vision-language models on image, video and spatial tasks.
- Fine-tuning a compact 2B or 9B VLM on custom image-text data.
Topics
FAQ
- What is NEO?
- Open-source native vision-language models (NEO series) that process pixels and words in one encoder-free dense architecture, with 2B and 9B releases.
- Is NEO free to use? What license does it have?
- NEO is open source under the Apache-2.0 license. Check the license terms before using it in commercial products.
- Is NEO still maintained?
- Updated within 6 months. The last commit to NEO was on Jul 27, 2026.
- What can I use NEO for?
- Researching native or encoder-free multimodal model design. Benchmarking vision-language models on image, video and spatial tasks. Fine-tuning a compact 2B or 9B VLM on custom image-text data.
- Where can I get NEO?
- The source code and installation instructions are on GitHub: https://github.com/EvolvingLMMs-Lab/NEO
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