A static quantized version of UME-R1-7B, supporting multiple tasks such as sentence similarity, embedding, zero-shot image classification, and video text-to-text. It offers various quantization types to meet different needs, from the lightweight Q2_K to the high-quality Q8_0 version.

MultimodalTransformersEnglish

Qwen3-Coder-REAP-363B-A35B is a sparse mixture-of-experts model obtained by pruning 25% of the experts from Qwen3-Coder-480B-A35B-Instruct using the REAP method. While maintaining performance close to the original model, it significantly reduces the parameter scale and memory requirements, making it particularly suitable for code generation and intelligent coding scenarios with limited resources.

Natural Language ProcessingTransformersEnglish

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

This is the MXFP4_MOE quantized version of the aquif-3.5-Plus-30B-A3B model. Through quantization technology, the model's resource occupancy is significantly reduced while maintaining similar performance, which has high engineering practice value.

Natural Language ProcessingGguf

This project is the MXFP4_MOE quantized version of the Kanana 1.5-15.7B-A3B instruction model, which can improve the model's operating efficiency in specific scenarios. This model is optimized based on kakaocorp/kanana-1.5-15.7b-a3b-instruct, using a mixture-of-experts architecture and MXFP4 quantization technology.

Natural Language ProcessingGguf

This is a text generation model optimized using PEFT (Parameter-Efficient Fine-Tuning) and LoRA techniques based on the Google Gemma-2B-IT base model. The model is optimized for specific tasks while maintaining the capabilities of the base model through parameter-efficient methods.

Natural Language ProcessingPeft

This is a GGUF format version converted from the Qwen3-VL-30B-A3B-Instruct model, specifically optimized for llama.cpp. This model is a multimodal visual language model with 30 billion parameters, supporting image understanding and text generation tasks.

MultimodalGguf

Melta is a unique small language model developed based on the Discord bot Melta27. It has a cute, charming, and enthusiastic personality, and is specifically designed to generate original, unique, and cute text content.

Natural Language ProcessingTransformersFrench

This is a static quantized version of the yanolja/YanoljaNEXT-Rosetta-27B-2511 model, providing weight files of various quantization types and supporting multilingual translation tasks. The model is specifically optimized for 32 languages, including major languages such as Chinese, English, Japanese, and Korean.

Natural Language ProcessingTransformersMultiple Languages

Qwen-Edit-Skin is a LoRA model fine-tuned based on the Qwen/Qwen-Image-Edit-2509 model, focusing on enhancing the realism and details of human skin in images and generating more natural and delicate skin textures.

Computer VisionDiffusers

Apollo-V0.1-4B-Thinking is a large language model based on 4B parameters, adopting a chain of thought reasoning architecture and specifically optimized for reasoning tasks. This model offers multiple quantization versions and can run efficiently in different hardware environments.

Natural Language ProcessingGguf

This is a quantized version model based on Qwen3-VL-30B, specifically designed for image-text to text conversion tasks. The model optimizes the original model through quantization technology, providing a more efficient inference solution.

MultimodalGguf

This is a quantized image-text-to-text model based on the Mistral architecture, with a parameter scale of 24B. It is specifically optimized and trained for instruction following tasks and supports multimodal input processing.

MultimodalGguf

Qwen3-VL is the most powerful vision-language model in the Tongyi series. It has excellent text understanding and generation capabilities, in-depth visual perception and reasoning capabilities, long context support, strong spatial and video dynamic understanding capabilities, and outstanding agent interaction capabilities.

MultimodalGguf

This is a quantized version of the Huihui Qwen3-VL-8B thinking ablated model. Based on the Qwen3-VL-8B architecture, it is specifically optimized and quantized for image-text conversion tasks and can be efficiently run through the llama.cpp tool.

MultimodalGguf

The quantized version of the Huihui Qwen3-VL-8B instruction ablation model, specifically designed for image-text to text conversion tasks. This model is based on the Qwen3-VL-8B architecture, optimized through instruction ablation and quantized, which can reduce the computational resource requirements while maintaining performance.

MultimodalGguf

The quantized version of the Huihui Qwen3-VL-4B thinking elimination model, optimized based on the Qwen3-VL-4B model, is specifically designed to eliminate the thinking chain reasoning process and can be used with the latest llama.cpp to provide efficient image-text to text processing capabilities.

MultimodalGguf

This is a quantized version based on the Huihui-Qwen3-VL-4B-Instruct-abliterated model, specifically designed for image-text to text conversion tasks. The model performance is optimized through quantization processing, providing support for multimodal applications.

MultimodalGguf

Qwen3-VL-32B-Thinking is the most powerful vision-language model in the Qwen series, with excellent text understanding and generation capabilities, in-depth visual perception and reasoning capabilities, long context processing, spatial and video dynamic understanding capabilities, and outstanding agent interaction capabilities.

MultimodalTransformers

Qwen3-VL-8B-Thinking is the most powerful vision-language model in the Tongyi Qianwen series. It has excellent text understanding and generation capabilities, in-depth visual perception and reasoning capabilities, long context support, strong spatial and video dynamic understanding capabilities, and outstanding agent interaction capabilities.

MultimodalGguf