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

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

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

MambaVision is the first hybrid computer vision model combining the strengths of Mamba and Transformer. It enhances visual feature modeling by redesigning the Mamba formulation and incorporates self-attention modules in the final layers of the Mamba architecture to improve long-range spatial dependency modeling.

Computer VisionTransformers

The first hybrid computer vision model combining the advantages of Mamba and Transformer, enhancing visual feature modeling capability by reconstructing the Mamba formula

Computer VisionTransformers

The first hybrid computer vision model combining the strengths of Mamba and Transformer, enhancing visual feature modeling efficiency through reconstructed Mamba formulas and introducing self-attention modules at the end of the Mamba architecture to improve long-range spatial dependency modeling.

Computer VisionTransformers

This is a Vision Transformer (ViT) model fine-tuned on a legume dataset for identifying disease conditions in legume leaves.

Computer VisionSafetensors

LightGlue is an efficient keypoint detection and matching model for feature matching and pose estimation problems in computer vision.

Computer VisionTransformers