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 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

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 is a quantized version based on the Huihui-MiroThinker-v1.0-8B model, specifically optimized for text generation tasks. It improves inference efficiency through quantization technology while maintaining the model's performance.

Natural Language ProcessingGguf

This is a version of the MiroThinker-v1.0-30B model quantized using MXFP4_MOE imatrix, implemented based on mradermacher's imatrix. This quantized model maintains the text generation ability of the original model while improving inference efficiency through quantization technology. It is suitable for various application scenarios that require efficient text generation.

Natural Language ProcessingGguf

Aquif-3.5-Max-42B-A3B is a large language model with 42 billion parameters, which has undergone MXFP4_MOE quantization processing. It optimizes inference efficiency while maintaining high-quality text generation capabilities. This model is based on an advanced mixture-of-experts architecture and is suitable for various natural language processing tasks.

Natural Language ProcessingGguf

Lamapi/next-12b is a multilingual large language model with 12 billion parameters, offering multiple quantized versions and supporting various natural language processing tasks such as text generation, question answering, and chatting. This model is trained on multiple domain datasets and features high efficiency and lightweight characteristics.

Natural Language ProcessingTransformersMultiple Languages

P1 - 30B - A3B is a large language model with 30 billion parameters. It has been processed by MXFP4_MOE quantization, which optimizes the inference efficiency while maintaining the model performance and is suitable for text generation tasks.

Natural Language ProcessingGguf

This is a quantized version of the Alibaba Tongyi Deep Research 30B-A3B model. It uses MXFP4_MOE quantization technology and additionally adds imatrix quantization, aiming to optimize model performance and resource utilization efficiency, and is suitable for text generation tasks.

Natural Language ProcessingGguf

This is the MXFP4_MOE quantized version of the Qwen3-Coder-REAP-25B-A3B model, specifically designed for text generation tasks. This model is based on the 25B parameter Qwen3-Coder architecture, and the storage and inference efficiency are optimized through the MXFP4 quantization technology while maintaining good performance.

Natural Language ProcessingGguf

This is the MXFP4_MOE quantized version of the Ling-flash-2.0 model, specifically optimized for text generation tasks. This version uses the MXFP4_MOE quantization technology to significantly reduce the model size while maintaining the model's performance and improve the inference efficiency.

Natural Language ProcessingGguf

RND1 is an experimental diffusion language model with 30 billion parameters, adopting a sparse mixture of experts architecture. This model is converted from a pre-trained autoregressive base model, supporting diffusion-based text generation. Only 3 billion parameters are activated for each token, achieving a balance between computational efficiency and model capacity.

Natural Language ProcessingTransformers

DeepSeek-V3.2-Exp-AWQ is a quantized version based on the DeepSeek-V3.2-Exp model, which achieves efficient text generation through the vLLM framework. This model introduces the DeepSeek sparse attention mechanism, significantly improving the efficiency of long context training and inference while maintaining the quality of the model output.

Natural Language ProcessingTransformers

The Transformer Library is a powerful natural language processing tool library that provides a rich set of pre-trained models and convenient usage interfaces. It is widely used in NLP tasks such as text generation, classification, and translation, greatly improving development efficiency.

Natural Language ProcessingTransformers

Llama-3.1-8B-onnx-ryzenai-npu is an optimized version developed by AMD based on Meta's Llama-3.1-8B model, specifically optimized for deployment on the AMD Ryzen AI NPU. Through Quark quantization, OGA model builder, and NPU-specific post-processing technology, this model significantly improves the inference efficiency on AMD hardware while maintaining high-quality text generation capabilities.

Natural Language ProcessingOnnxEnglish

This model is the 8-billion parameter version in the Llama 3.1 series released by Meta. It has been processed and post-processed by the AMD Quark quantization tool, optimizing the deployment and inference efficiency on specific hardware. It is a multilingual text generation model that supports multiple languages and performs excellently in benchmark tests such as MMLU.

Natural Language ProcessingOnnxMultiple Languages

This is the 8-bit AWQ quantized version of the Qwen3-Next-80B-A3B-Thinking model. It optimizes inference efficiency through advanced quantization technology, significantly reducing resource consumption while maintaining model performance. It is suitable for large-scale text generation tasks.

Natural Language ProcessingTransformers

Qwen Image Edit Plus GGUF is an image editing project based on the Qwen model, offering multiple running modes and supporting image editing and generation tasks. This project optimizes model deployment and running efficiency through the GGUF format.

MultimodalGguf

This is a LoRA model that fine-tunes SD3.5-Medium using the DiffusionNFT method. It adopts an innovative online reinforcement learning paradigm and performs excellently in the text-to-image generation task, significantly improving the generation quality and training efficiency.

Computer VisionDiffusers

A quantized version based on Stable Diffusion XL Base 1.0, achieving 4-bit quantization through SVDQuant technology, significantly improving inference efficiency while maintaining high-quality image generation capabilities.

Computer VisionDiffusersEnglish