NVlabs/Eagle
Eagle: Frontier Vision-Language Models with Data-Centric Strategies
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Where it stands
Rank, growth pace and release cadence relative to everything GStars tracks.
The project lists 10 contributors on GitHub.
Peaked at #30 in the weekly ranking the week of Jun 28, its best in the past year.
Use Cases & Benefits
- Develops frontier vision-language models using data-centric strategies for multimodal understanding, long-context reasoning, and embodied AI applications.
- Offers state-of-the-art vision-language grounding and long-context multimodal reasoning with efficient parallel box decoding for fast, high-quality inference.
- Use for building generalist vision-language grounding systems that perform dense object detection, document understanding, and GUI element localization.
- Use for developing long-context video and image understanding applications that require detailed captioning and sectioning of visual content.
- Use for integrating vision-language models into robotics and embodied AI for 3D perception, spatial intelligence, and smart city pedestrian detection.
Should you depend on it?
License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.
License
Apache 2.0
Permissive: commercial use, modification and redistribution are allowed with attribution; includes an explicit patent grant from contributors.
Community files
- README: present
- Contributing guide: present
Watch-outs
- No GitHub releases; pin a commit or tag instead of a release.
General guidance from repository metadata, not legal advice. How we score
From the README
Eagle is a family of frontier vision-language models (VLMs) from NVIDIA that explore data-centric strategies across general-purpose multimodal understanding, long-context reasoning, and embodied applications.
Beyond advancing multimodal foundation model research, Eagle has also served as a research and development platform that supports multiple flagship NVIDIA efforts across enterprise intelligence and Physical AI, including but not limited to Llama-Nemotron Nano VLM, Nemotron VLMs (V2 Nano VL / V3 Nano Omni), NeMo Retriever (Llama Nemoretriever Colembed), NVIDIA Isaac GR00T N1 (Tech Report) / N1.5 / N1.6, and Cosmos.
Daily snapshots
Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots
Forks
368+45
Open issues
66+4
Compared with peers in NLP
Fastest-growing repositories GStars tracks in this cohort for the week of Nov 1, by 7-day star growth
| Repository | Stars | +7d | Health | Last release |
|---|---|---|---|---|
| ruvnet/ruflo | 81.0K | +847 | 40 | today |
| mudler/LocalAI | 50.2K | +176 | 59 | 12 days ago |
| 666ghj/BettaFish | 42.5K | — | 44 | 308 days ago |
| d2l-ai/d2l-en | 30.0K | — | 45 | — |
| QwenLM/Qwen | 21.9K | — | 24 | — |
| NVlabs/Eagle | 3.8K | — | 16 | — |
| Category median | 42.5K | +512 | 44 | — |
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