# Safetensors

EXAONE 3.5 is a series of instruction-tuned bilingual (English and Korean) generative models developed by LG AI Research, with parameters ranging from 2.4B to 32B. These models support long context processing of up to 32K tokens and demonstrate state-of-the-art performance in real-world use cases and long context understanding, while remaining competitive in general domains compared to similarly sized models released recently. The EXAONE 3.5 models include: 1) the 2.4B model, optimized for deployment on small or resource-constrained devices; 2) the 7.8B model, matching the size of predecessor models but offering improved performance; 3) the 32B model, providing powerful performance.

EXAONE-3.5-2.4B-Instruct is a series of bilingual (English and Korean) instruction-tuned generation models developed by LG AI Research, with parameter sizes ranging from 2.4B to 32B. These models support long context processing of up to 32K tokens and demonstrate state-of-the-art performance in real-world use cases and long context understanding while remaining competitive in general domains compared to similarly sized recently released models. The model is particularly suited for scenarios that require processing long texts and multilingual needs, such as automatic translation, text summarization, and conversational systems.

InternVL2_5-4B is an advanced multimodal large language model (MLLM) that maintains the core model architecture of InternVL 2.0 while significantly enhancing training and testing strategies and data quality. The model excels in handling tasks from image and text to text, particularly in multimodal reasoning, mathematical problem solving, OCR, and chart and document comprehension. As an open-source model, it provides researchers and developers with powerful tools to explore and build intelligent applications based on visual and linguistic elements.

InternVL2_5-8B is a multimodal large language model (MLLM) developed by OpenGVLab, significantly enhanced with training and testing strategies as well as data quality improvements based on InternVL 2.0. This model employs the 'ViT-MLP-LLM' architecture, integrating the newly pre-trained InternViT with various pre-trained language models, such as InternLM 2.5 and Qwen 2.5, utilizing a randomly initialized MLP projector. The InternVL 2.5 series models demonstrate outstanding performance on multimodal tasks, including image and video understanding and multilingual comprehension.

RMBG-2.0 is a background removal model developed by BRIA AI, aimed at effectively separating the foreground and background in images. The model is trained on a curated dataset including general stock images, e-commerce, gaming, and advertising content, making it suitable for commercial use and capable of driving large-scale content creation for enterprises. Its accuracy, efficiency, and versatility are comparable to leading open-source models. RMBG-2.0 is available as source code for non-commercial use.