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

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

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 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

This is the GGUF quantized version of ArliAI's GLM-4.5-Air-Derestricted model. It uses llama.cpp and imatrix technology for optimized quantization processing, offering multiple quantization levels to suit different hardware configurations and performance requirements.

Natural Language ProcessingGguf

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

This is an MXFP4_MOE imatrix quantized version based on the Huihui-MiroThinker-v1.0-30B model, specifically optimized for text generation tasks. It significantly reduces the model size and inference cost while maintaining model performance.

Natural Language ProcessingGguf

This is a quantized version of TheDrummer's Snowpiercer-15B-v4 model, quantized using llama.cpp and imatrix technology. This version offers multiple quantization levels from BF16 to IQ2_S, meeting the needs of users with different hardware conditions and performance requirements. The model is calibrated and optimized using a specific dataset and supports the online repackaging function to improve performance on ARM and AVX hardware.

Natural Language ProcessingGguf

Cecilia FT MS v1 is a Cuban language model fine-tuned based on Cecilia 2B v0.1, specifically optimized for Cuban Spanish to capture the nuances of Cuban language, culture, and society. This model supports Spanish and English and is mainly used for text generation tasks.

Natural Language ProcessingTransformersMultiple Languages

Olmo-3-7B-Instruct-AIO-GGUF is a GGUF quantized version based on the Olmo-3-7B-Instruct model developed by the Allen Institute for AI. This is an autoregressive language model with 7 billion parameters, trained on datasets such as Tulu 2 and UltraFeedback through supervised fine-tuning and direct preference optimization, and performs excellently in question-answering and instruction following.

Natural Language ProcessingTransformersEnglish

GLM-4.5-Air-REAP-82B-A12B-mxfp4-mlx is a large language model with 82B parameters compressed by the REAP method based on the GLM-4.5-Air model. It uses the MXFP4 quantization format and is optimized for Mac devices, significantly reducing memory usage while maintaining the performance of core tasks.

Natural Language ProcessingMlxEnglish

This is an automatic speech recognition model optimized for Apple silicon chip devices. By converting to the MLX framework and quantizing to the FP8 format, it enables fast on-device speech transcription on Apple devices. The model is fine-tuned for verbatim accuracy and is particularly suitable for scenarios requiring high-precision transcription.

Audio ProcessingMlxOther

This is a video understanding model fine-tuned on an unknown dataset based on the MCG-NJU/videomae-base model. After 20 epochs of training, it achieved an accuracy of 13.31% on the evaluation set. This model is specifically optimized for video analysis tasks.

Computer VisionTransformers

The quantized GGUF version of Tencent's Hunyuan Video 1.5 model, specifically designed for text-to-video generation tasks, supports 720P HD video generation, and offers multiple quantization precision versions to optimize usage efficiency.

MultimodalGguf

This project provides a quantized GGUF version of the Tencent HunyuanVideo-1.5-I2V-720p model, specifically designed for image-to-video and video generation tasks. The model supports converting static images into high-quality video content and offers multiple quantized versions to optimize performance.

MultimodalGguf

A quantized model optimized for HunyuanVideo-1.5, used in conjunction with the LightX2V framework, which significantly reduces memory usage while maintaining high-quality video generation performance.

MultimodalDiffusers

This is the GGUF quantized version of the ai-sage/GigaChat3-10B-A1.8B-bf16 model, offering a variety of quantization options, from high-precision Q8_0 to extremely compressed smol-IQ1_KT, to meet the deployment requirements under different hardware conditions. This model supports a 32K context length, adopts the MLA architecture, and is optimized for dialogue scenarios.

Natural Language ProcessingGgufOther

This project is a medical domain model created using pretrained language model fusion technology. By fusing multiple medical-related pretrained models, it is specifically optimized for medical Q&A tasks, improving performance and effectiveness in medical scenarios.

Natural Language ProcessingSafetensorsEnglish