ActIO-UI-7B-RLVR is a 7-billion-parameter visual language model released by Uniphore, specifically designed for computer interface automation tasks. It is based on Qwen2.5-VL-7B-Instruct and optimized through supervised fine-tuning and reinforcement learning with verifiable rewards. It performs excellently in tasks such as GUI navigation, element positioning, and interaction planning, and has achieved the leading level among open-source 7B models in the WARC-Bench benchmark test.

MultimodalTransformersEnglish

This is an object detection model fine-tuned on a custom dataset based on the YOLOv12 Medium architecture. This model is specifically designed to efficiently and accurately detect drone targets in images or videos, providing support for computer vision applications.

Fara-7B is an efficient small language model specially designed by Microsoft for computer usage scenarios. It has only 7 billion parameters and performs excellently in advanced user tasks such as web operations, competing with larger agent systems.

MultimodalGguf

Gelato-30B-A3B is a state-of-the-art (SOTA) model fine-tuned for GUI computer usage tasks, offering a quantized version to optimize deployment efficiency. This model is specifically designed to understand and process tasks related to graphical user interfaces.

MultimodalGguf

Fara-7B is a small language model developed by Microsoft Research, specifically designed for computer usage scenarios. It has only 7 billion parameters and achieves excellent performance among models of the same scale. It can perform computer interaction tasks such as web automation and multimodal understanding.

MultimodalTransformersEnglish

Gaperon-Young-1125-1B is a bilingual (French-English) language model with 1.5 billion parameters, developed by the ALMAnaCH team at the French National Institute for Research in Computer Science and Control (Inria Paris). The model is trained on approximately 3 trillion high-quality tokens, with a particular focus on language quality and general text generation ability rather than benchmark test optimization.

Natural Language ProcessingTransformersMultiple Languages

Gelato-30B-A3B is a state-of-the-art foundation model for GUI computer usage tasks. It is trained on the Click-100k dataset and outperforms previous specialized computer foundation models and larger vision-language models in multiple benchmark tests.

MultimodalTransformersEnglish

OpenCUA is an end-to-end computer usage foundation model series, built on the Qwen2.5-VL instruction model, capable of generating executable operations in a computer environment. It has powerful visual positioning and multi-step task planning capabilities, and performs excellently in computer usage agent benchmark tests such as OSWorld.

MultimodalTransformersEnglish

This is a vision Transformer model based on the DINOv3 framework, trained on the LVD-1689M dataset from the DINOv3 ViT-7B model through knowledge distillation technology. This model is specifically designed for image feature encoding and can efficiently extract image feature representations, suitable for various computer vision tasks.

Computer VisionTimm

This is a vision Transformer model based on the DINOv3 architecture, using a small configuration and trained through knowledge distillation on the LVD-1689M dataset. This model is specifically designed for efficient image feature extraction and supports various computer vision tasks such as image classification, feature map extraction, and image embedding.

Computer VisionTimm

This is a computer vision model based on the YOLOv8s architecture, specifically designed for barcode and QR code detection. The model has been fine-tuned on a comprehensive dataset containing more than 5000 images, supporting accurate detection and classification of multiple barcode types (such as EAN13, Code128, etc.) and QR codes.

This is a computer vision model fine-tuned based on Ultralytics/YOLO11, specifically designed to detect UI elements in macOS application screenshots. It is part of the Screen2AX project, dedicated to generating accessibility metadata using computer vision technology.

A powerful computer vision tool capable of classifying, detecting, and extracting text from Indian ID card documents.

Computer VisionPytorchEnglish

An image-text to text generation model based on the Transformer architecture, designed specifically for computer/GUI-related scenarios, with intelligent agent capabilities.

MultimodalGgufEnglish

Zeta 2 is a small language model (SLM) with approximately 460 million parameters, meticulously crafted on consumer-grade computers and supports multiple languages.

Natural Language ProcessingSafetensorsMultiple Languages

This model is fine-tuned based on the DINOv2 architecture for disease classification of skin lesion images

Computer VisionPytorchEnglish

This is the ONNX format version of the facebook/dinov2-base model, suitable for computer vision tasks.

Computer VisionTransformers

This is a cross-encoder model fine-tuned from cross-encoder/ms-marco-MiniLM-L6-v2, developed using the sentence-transformers library. It can compute scores for text pairs and is suitable for text re-ranking and semantic search.

Natural Language ProcessingSafetensorsEnglish

This model is used to compute embedding vectors for Russian and English sentences, obtained by distilling the embedding vectors from ai-forever/FRIDA. The model is of the uncased type, meaning it does not distinguish between uppercase and lowercase letters in the text.

Natural Language ProcessingTransformersMultiple Languages

The first hybrid computer vision model combining the strengths of Mamba and Transformer, enhancing visual feature modeling efficiency by reconstructing the Mamba formula, and introducing self-attention modules in the final layers of the Mamba architecture to improve long-range spatial dependency modeling.

Computer VisionTransformers