Z-Image is an efficient basic image generation model with 6 billion parameters, specifically designed to address the efficiency and quality issues in the field of image generation. Its distilled version, Z-Image-Turbo, can reach or exceed the leading competitors with only 8 function evaluations. It can achieve sub-second inference latency on enterprise-level H800 GPUs and run on consumer-grade devices with 16G VRAM.

Computer VisionDiffusersMultiple Languages

Z-Image-Turbo is an image processing model quantized in FP8 E5M2 and E4M3FN formats. It is optimized based on the original Tongyi-MAI/Z-Image-Turbo model, significantly reducing the model size and inference resource requirements while maintaining performance.

MultimodalDiffusersEnglish

This is a video understanding model fine-tuned based on the VideoMAE-base architecture, specifically optimized for sign language recognition tasks. The model was trained on the WLASL dataset for 200 epochs, using the TimeSformer architecture to process video sequences.

Computer VisionTransformers

This is a video understanding model fine-tuned based on the VideoMAE-base architecture, specifically optimized for sign language recognition tasks. The model was trained on the WLASL100 dataset for 200 epochs and has the ability to recognize video actions.

Computer VisionTransformers

INTELLECT-3 is a Mixture of Experts (MoE) model with 106 billion parameters, trained through large-scale reinforcement learning. It demonstrates excellent performance in mathematics, coding, and reasoning benchmark tests. The model, training framework, and environment are all open-sourced under a permissive license.

Natural Language ProcessingTransformersEnglish

This is the quantized version of Qwen3-VL-32B-Thinking-heretic, using a new ablation method for quantization. Compared with traditional methods, it causes less damage to the base model. It offers multiple quantization precision versions and is suitable for running on devices with 24GB of VRAM, supporting image-to-text tasks.

MultimodalTransformersEnglish

This is a hybrid - language AI model specifically designed for call record analysis, capable of handling call transcription content in Hindi, English, and mixed Hindi - English. The model is fine - tuned based on Qwen2.5 - 1.5B - Instruct and has powerful multilingual understanding and information extraction capabilities.

Natural Language ProcessingTransformersMultiple Languages

This is a video understanding model fine-tuned on an unknown dataset based on the VideoMAE-base architecture, specifically designed for sign language recognition tasks. The model achieved an accuracy of 18.64% after 20 training epochs.

Computer VisionTransformers

FLUX.2-dev is an image generation and editing model developed by black-forest-labs. It has been converted to the GGUF format, optimized specifically for image generation tasks, and adopts a diffusion model architecture. It supports use in the ComfyUI framework.

MultimodalGguf

flux2-dev-gguf is an image-to-image conversion model based on FLUX.2-dev, specifically designed to generate images in a specific style based on text prompts. This model supports running in the ComfyUI environment and can convert text descriptions into stylized visual content.

MultimodalGgufEnglish

The Pikachu Language Model is a unique experimental language project initiated by Clemylia. It is trained completely from scratch and is based solely on the sound corpus of 'Pikachu', demonstrating the ability of small language models to create a native language identity.

Natural Language ProcessingTransformers

An optimized image generation model based on black-forest-labs/FLUX.2-dev, supporting image-to-image generation and editing tasks. It uses fp8_scaled quantization technology to improve inference speed and is particularly suitable for use on low-VRAM GPUs.

This is a text-to-image conversion model based on LoRA technology, specifically designed to generate images with the artistic style of French Impressionist painter Berthe Morisot. This model is trained on the FLUX.2-dev base model and can convert ordinary images or text descriptions into paintings in Morisot's style.

Computer VisionDiffusers

Z-Image is a powerful and efficient image generation model with 6 billion parameters. It can effectively solve the problems in the fields of image generation in terms of efficiency, quality, and functional diversity, providing users with high-quality image generation and editing services.

Computer VisionDiffusersEnglish

TomoroAI/tomoro-colqwen3-embed-4b is an advanced ColPali-style multimodal embedding model that can map text queries, visual documents (such as images, PDFs) or short videos into aligned multi-vector embeddings. This model combines the advantages of Qwen3-VL-4B-Instruct and Qwen3-Embedding-4B, performs excellently in the ViDoRe benchmark test, and significantly reduces the embedding space occupation.

MultimodalTransformersOther

Wan2.2 is a major upgrade version of the basic video model, focusing on integrating innovative technologies such as effective MoE architecture, efficient training strategies, and multimodal fusion into the video diffusion model, bringing more powerful and efficient solutions to the field of video generation.

MultimodalDiffusersMultiple Languages

This is a video understanding model based on the VideoMAE architecture. It has been fine-tuned on the basis of pre-training on the Kinetics dataset and is specifically designed for sign language recognition tasks. The model's performance on the evaluation set needs improvement, with an accuracy of 0.0010.

Computer VisionTransformers

This is a quantized version of the Microsoft Fara-7B model, optimized using the imatrix technology of llama.cpp. The model supports multiple quantization levels, from high-precision bf16 to extremely low-precision IQ2_M, meeting the needs of different hardware configurations and performance requirements.

Natural Language ProcessingGguf

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