brats_medgemma_light is a fusion model based on unsloth/medgemma-4b-it, fine-tuned on the BraTS and TextBraTS datasets. It is a lightweight vision-language model specifically designed for brain MRI interpretation and radiology text generation.

MultimodalTransformersEnglish

This is a LyCORIS adapter based on the Qwen/Qwen-Image model, specifically designed for text-to-image and image-to-image generation tasks. The model did not use validation prompts during training and adopted optimized training configurations and quantization techniques.

MultimodalDiffusers

This is the MXFP4_MOE quantized version of the GLM-4.5-Air-REAP-82B-A12B model, specifically optimized for text generation tasks. This model is based on the GLM-4.5-Air-REAP-82B-A12B base model of Cerebras and is implemented through MXFP4 mixed expert quantization technology, significantly reducing the model size and inference cost while maintaining performance.

Natural Language ProcessingGguf

This is the MXFP4_MOE quantized version of the Qwen3-30B-A3B-CoderThinking-YOYO-linear model, which is specifically optimized for text generation tasks. The model achieves model compression through MXFP4 quantization technology while maintaining good performance.

Natural Language ProcessingGguf

This is the quantized version of the PokeeAI/pokee_research_7b model, adhering to the concept of 'Making knowledge accessible to everyone', and reducing the resource requirements for model deployment and use through quantization technology.

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

Qwen-Image-Edit-MeiTu is an improved version based on Qwen-Image-Edit. It is fine-tuned using the DiT architecture and focuses on enhancing visual consistency, aesthetic quality, and structural alignment capabilities in complex image editing.

Computer VisionDiffusersMultiple Languages

Fathom-Search-4B GGUF is a powerful tool designed for text generation tasks. It is generated based on specific base models and technologies and performs excellently in search-intensive benchmark tests. It can effectively solve the problems of long-cycle information retrieval and synthesis.

Natural Language ProcessingTransformers

A quantized version of the text-to-image generation model developed based on Flux.1, using SVDQ quantization technology, offering two quantization formats, INT4 and FP4, suitable for efficient image generation on different hardware platforms.

Computer VisionDiffusersEnglish

This is a quantized version of the allenai olmOCR-2-7B-1025 model. Multiple quantized model files are generated using the llama.cpp tool, which is convenient for users with different hardware conditions and requirements. This model supports running in LM Studio or projects based on llama.cpp.

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

Lam-2 is the second-generation small language model (SLM) developed based on the custom architecture Aricate V4. It performs excellently in question and answer tasks and has outstanding language coherence and creativity. Compared with the previous generation products, Lam-2 has significantly improved in grammatical correctness and text generation quality.

Natural Language ProcessingSafetensorsFrench

This is the Q4_K_M gguf quantized version of the GLM-4.6-REAP-266B-A32B model, built on the transformers library, with text generation capabilities. The model uses the REAP (Routing-Weighted Expert Activation Pruning) method, which can selectively remove redundant experts while retaining the router's independent control over the remaining experts.

Natural Language ProcessingTransformers

Nanbeige4-3B-Thinking is a 3-billion parameter inference model in the fourth-generation Nanbeige large language model family. It achieves advanced inference capabilities by improving data quality and training methods. This model performs excellently in multiple fields such as mathematics, science, creative writing, and tool usage, and supports multi-stage curriculum learning and reinforcement learning training.

Natural Language ProcessingTransformersMultiple Languages

A quantized version of the text-to-image generation model developed based on Flux.1-Dev of J1B, optimized using SVDQuant technology, offering two quantization formats, INT4 and FP4, suitable for users with different GPU architectures.

Computer VisionDiffusersMultiple Languages

LFM2-VL-3B is a multimodal vision-language model developed by Liquid AI, built on the LFM2 backbone architecture. It has powerful visual understanding and reasoning capabilities, especially excelling in fine-grained perception tasks. This model can efficiently process text and image inputs and supports native image processing with a resolution of up to 512×512.

MultimodalTransformersMultiple Languages

This project is the result of MXFP4_MOE quantization of the Qwen3-VL-30B-A3B-Thinking model. This model can achieve the conversion from image-text to text, providing a new solution for applications in related fields.

MultimodalGguf

This project is the result of MXFP4_MOE quantization on the Qwen3-VL-30B-A3B-Instruct model. This model can achieve the conversion from image text to text, providing a new solution for applications in related fields. As of October 22, 2025, this model is still in the experimental stage.

MultimodalGguf

This is the Q2_K_M gguf quantized version of the GLM-4.6-REAP-266B-A32B model, created based on the REAP (Router Weighted Expert Activation Pruning) method. It can selectively remove redundant experts while retaining the router's independent control over the remaining experts, achieving efficient text generation.

Natural Language ProcessingTransformers

Qwen3-VL is the most powerful vision-language model in the Qwen 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.

MultimodalTransformers