NVIA AI emits Eagle2 Series Movie-language of text: Finding Sota Outcomes in all different Multimodal benches

The models belonging to the identity language (VLMs) are highly expanded AI's ability to process multimedal information, but they suffer further challenges. Models relating to GPT-4V and Gemini-1.5-Pro ​​to achieve amazing performance but did nothing, reducing their change. Other open ways often strive to match these types due to problems for data variation, training methods and computer resources. Additionally, the documents are limited to the training database of the training data after writing. Dealing with these posts, Unvidia Ai introduces Eagle 2The VLM designed in a systematic, transparent in the transformation and moderation of model and training.

NVIDIA AI introduces Eagle 2: Expressed VLM

Eagle 2 offers a new method by prioritization to its data plan. Unlike most models that provide training only, details of the Eagle 2 Collection of data, sorting, added, and selection processes. The program aims to equip open-open society and vlama development tools without depending on the datasets relating.

Eagle2-9B, the highest quality model in Eagle 2 Series, do in multiple times and models its size, such as those with 70b parameters. By being explained by the training techniques after training, Eagle 2 increases operation without needing social resources.

Important establishment in Eagle 2

Eagle 2 Stem Power from New New Nountry: Surnged strategy for data, multilingual training, and ventic construction.

1. Data strategy
   • The model follows a Differences – first, and quality Come near, simplify data from above 180 sources Before you listed it by filtering and selection.
   • A formal data analysis pipe includes error analysis, chain-hold (cot) descriptions, qa production based on QA, and data format.
2. The three-class training framework
   • Section 1 Sign up the vision and language languages ​​by training the MLP connector.
   • Section 1.5 Introducing a variety of data, emphasizes the basis of model.
   • Section 2 Fine-runs model using higher shipping datasets.
3. A tiled combination of Vision Encoders (Move)
   • The model meets SIGLIP and CONVEXTXT As two texts to see, improve the understanding of the image.
   • The til-resolution til-resolution confirms good details are well kept.
   • The Stringest Way – Aving-Paill Knapsack reduces data management, reducing training costs while improving sample performance.

These things make eagle 2 Both powerful and adapt to different programs.

Working and Light of Benchmark

Eagle 2 skills are tested firmly, showing strong performance on all many benches:

• Eagle2-9B reaches 92.6% accuracy in DocvqqaExceeding an Intervl2-8B (91.6%) and GPT-4V (88.4%).
• In OcrbelchEagle 2 Scores 868QWenFormF-VL-7B (845) and minicM-v-2.6 (852), highlights its ability to hire text.
• Mathvista's operation It promotes over 10 points Compared with its basis, emphasizes the effectiveness of the three-class method.
• Chartqa, OCR QA, and Multimodal consultation activities Show significant improvements, GPT-4V excelling in key areas.

In addition, the training process is designed to work properly. Top Download Techniques Demonstrated data size from 12.7m to 4,6m samplesKeeping the accuracy while developing data effectiveness.

Store

Eagle 2 represents a step forward to making VLMs very easily accessible and cany. By emphasis The form of the transparent dataClosing the gap between the open source and functioning of models concerning models. New Model Things In Data Scheme, Training Methods, and the Construction of the Vision Make it a force for coercion and enhancements.

By sharing in the order of its road, Nvidia Ai Toununders a AI Research LocationAllowing the public to build on these discreet things without relying on the closed source models. Since AI continues to appear, E ea is an example that strategies for understanding and training can lead to strong models, which they make unparted languages.

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