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 repository contains a 4-step distilled model optimized for HunyuanVideo-1.5, which can achieve ultra-fast 4-step inference without using CFG (Classifier-Free Guidance), significantly reducing the generation time while maintaining high-quality video output.

MultimodalDiffusers

Qwen3-32B is the latest generation of large language models in the Qwen series, with powerful reasoning, instruction-following, intelligent agent interaction, and multilingual processing capabilities. It supports over 100 languages and dialects and can seamlessly switch between thinking and non-thinking modes, providing users with a natural and smooth dialogue experience.

Natural Language ProcessingTransformers

Cyber Intelligence Brain is a large language model specifically fine-tuned for Capture the Flag (CTF) challenges and network security tasks. It is based on the Llama-3.1-8B-Instruct architecture and can assist in solving CTF problems, analyzing security vulnerabilities, conducting reverse engineering and cryptography challenges, and providing vulnerability exploitation reasoning while adhering to ethical guidelines.

Natural Language ProcessingSafetensorsMultiple Languages

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

This is an experimental Stable Diffusion 1.5 distillation model that uses the v-prediction flow matching method and geometric guided adaptive block weighting technology. It is currently in the research phase, the training is ongoing, and the results have not been verified.

Computer VisionDiffusers

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

Qwen2.5-14B-Instruct is a multilingual large language model based on the Qwen2.5 architecture, with 14 billion parameters. It supports 13 languages and has excellent instruction-following and dialogue capabilities. This model performs well in various language tasks and is suitable for text generation and understanding in multilingual scenarios.

Natural Language ProcessingTransformersMultiple Languages

This is the first - generation powerful vibe - code large language model, optimized to directly generate natural language and code completion based on loosely structured 'atmosphere coding' prompts. Compared with early models, it has lower prompt engineering overhead, smoother latent space interpolation, and is easier to guide the generation of usable code.

Natural Language ProcessingTransformersEnglish

GTA1 is a GUI grounding model trained based on GRPO reinforcement learning, specifically designed for graphical user interface automation operations. Compared with methods that rely on long chains of thought reasoning, GRPO directly incentivizes actionable and well-founded responses, demonstrating excellent grounding performance in multiple benchmark tests.

MultimodalTransformersEnglish

This is a transformer model published on the Hugging Face model hub. Specific information is to be supplemented by the developer. The model card is automatically generated by the system, providing the basic framework and usage guide of the model.

Natural Language ProcessingTransformers

GTA1 is a state-of-the-art GUI grounding model trained based on reinforcement learning (GRPO), specifically designed for graphical user interface automation tasks. Different from methods that rely on long thought-chain reasoning, GRPO directly incentivizes actionable and well-founded responses, demonstrating excellent grounding performance and agent performance on multiple challenging datasets.

MultimodalTransformersEnglish

Qwen2.5-14B-Instruct is a large language model with 14 billion parameters, designed specifically for chat and text generation scenarios. This model is built based on the transformers library and is suitable for internal testing and lightweight application deployment.

Natural Language ProcessingTransformersEnglish

InternVL3.5-4B is a medium-scale version in the open-source multimodal model series, achieving significant progress in generality, reasoning ability, and reasoning efficiency, and supporting new capabilities such as GUI interaction. The model adopts a cascaded reinforcement learning framework and visual resolution router technology to achieve efficient multimodal understanding and reasoning.

MultimodalTransformersOther

InternVL3.5-14B is an open-source multimodal model in the InternVL series, which significantly improves generality, reasoning ability, and reasoning efficiency, supports new functions such as GUI interaction, and narrows the performance gap with commercial models.

MultimodalTransformersOther

InternVL3.5-1B is an open-source multimodal model in the InternVL series, with a parameter count of 1.1B, including 0.3B visual parameters and 0.8B language parameters. This model significantly improves generality, reasoning ability, and reasoning efficiency, and supports new functions such as GUI interaction.

MultimodalTransformersOther

InternVL3.5 is a new member of the open-source multimodal model family, significantly improving the versatility, reasoning ability, and reasoning efficiency of the InternVL series. It supports new functions such as GUI interaction and reaches the advanced level of open-source multimodal large language models.

MultimodalTransformersOther

MedGemma-GRPO is a fine-tuned version of the MEDGemma model, specifically designed for clinical case reasoning tasks. This model combines supervised fine-tuning (SFT) and group relative policy optimization (GRPO) to guide the model to follow logical diagnostic paths. It is trained on a synthetic medical case dataset and is particularly suitable for diagnostic reasoning of complex cardiovascular, thrombotic, and autoimmune diseases.

Natural Language ProcessingTransformersEnglish

FLUX.1 Krea [dev] is a rectified flow transformer with 12 billion parameters, capable of generating high-quality images based on text descriptions. This model focuses on aesthetic photography and excels in image quality and instruction following. It uses guided distillation for efficient training and opens its weights to promote scientific research and artistic creation.

MultimodalDiffusersEnglish

FLUX.1 Krea [dev] is an advanced rectified flow transformer model with 12 billion parameters, specifically designed to generate high-quality images based on text descriptions. This model excels in the field of aesthetic photography, has excellent prompt-following ability, is trained using guided distillation technology, and its weights are open for non-commercial use.

Computer VisionDiffusersEnglish