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Eagle2-1B

NVIDIA

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Eagle2-2B

NVIDIA

We are thrilled to release our latest Eagle2 series Vision-Language Model. Open-source Vision-Language Models (VLMs) have made significant strides in narrowing the gap with proprietary models. However, critical details about data strategies and implementation are often missing, limiting reproducibility and innovation. In this project, we focus on VLM post-training from a data-centric perspective, sharing insights into building effective data strategies from scratch. By combining these strategies with robust training recipes and model design, we introduce Eagle2, a family of performant VLMs. Our work aims to empower the open-source community to develop competitive VLMs with transparent processes. In this repo, we are open-sourcing Eagle2-2B, a lightweight model that achieves remarkable efficiency and speed while maintaining solid performance. We provide a inference script to help you quickly start using the model. We support different input types: - pure text input - single image input - multiple image input - video input

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Eagle2-9B

NVIDIA

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EGM-4B

NVIDIA

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EGM-8B

NVIDIA

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FasterViT

NVIDIA

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Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.

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Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.

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Rankings depend on tasks, versions and methods. Compare accuracy, latency, reliability and costs on your own workload.

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