This is a Dutch patient simulation model fine-tuned based on Google's Gemma 2 9B model, specifically designed for medical education scenarios. The model always responds in a fixed JSON structure, simulating a patient 'Maria' who has just completed surgery and is in the recovery process, supporting nursing students to exercise communication and clinical reasoning skills in a safe environment.

Natural Language ProcessingGgufOther

This is an English-Spanish translation model fine-tuned on the Amazon dataset based on the Google mT5-small model, specifically designed for text abstract generation tasks. The model achieved scores of Rouge1: 16.44 and Rouge2: 8.04 on the evaluation set.

Natural Language ProcessingTransformers

Prima-24B is a large language model with 24 billion parameters, which is quantized and optimized through the GGUF format and specifically optimized for role-playing and creative writing tasks. This model is converted from the original Prima-24B model, using the Q4_K_M quantization level, which significantly reduces the model size and memory requirements while maintaining good performance.

Natural Language ProcessingGgufEnglish

FLAN-T5 Small is an instruction fine-tuned text generation model developed by Google, based on the small version of the T5 architecture. This model has undergone instruction fine-tuning on various NLP tasks and can understand and execute various text generation instructions.

Natural Language ProcessingSafetensorsEnglish

Chandra is a high-precision OCR model that can convert images and PDFs into structured outputs, such as Markdown, HTML, and JSON, while retaining detailed layout information. It supports more than 40 languages and is good at handling complex document elements.

MultimodalTransformersEnglish

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

These are various GGUF format quantized versions of the Google embeddinggemma-300m base model, optimized for efficient deployment and use in different scenarios. This model focuses on sentence embedding and similarity calculation tasks.

Natural Language ProcessingGgufEnglish

This project performs 4-bit quantization on the MiniMax-M2 model using the DWQ (Dynamic Weight Quantization) method with the help of the mlx-lm library. This model is a lightweight version of MiniMax-M2, significantly reducing the model size while maintaining good performance.

Natural Language ProcessingMlx

Kimi Linear is an efficient hybrid linear attention architecture that outperforms traditional full attention methods in short context, long context, and reinforcement learning scenarios. It optimizes attention computation through the Kimi Delta Attention (KDA) mechanism, significantly improving performance and hardware efficiency, and is particularly good at handling long context tasks up to 1 million tokens.

Natural Language ProcessingTransformers

This model is an instance segmentation model fine-tuned on the qubvel-hf/ade20k-mini dataset based on facebook/mask2former-swin-tiny-coco-instance. This model is specifically optimized for the scene understanding task in the ADE20K-mini dataset and shows good performance in the instance segmentation task.

Computer VisionTransformers

This is the MXFP4_MOE quantized version of the Qwen3-Yoyo-V4-42B-A3B-Thinking-TOTAL-RECALL model, specifically optimized for text generation tasks. The model is based on a large language model architecture with 42B parameters. Through quantization technology, it reduces the computational and storage requirements while maintaining good performance.

Natural Language ProcessingGguf

Scout is a professional language model fine-tuned by VANTA Research based on the Google Gemma 3 4B Instruct architecture. It excels in constraint-aware reasoning and adaptive problem solving, and is擅长侦察式情报收集 should be translated as is good at scouting-style intelligence gathering。擅长侦察式情报收集、系统问题分解和自适应解决方案生成。It is good at scouting-style intelligence gathering, system problem decomposition, and adaptive solution generation.

Natural Language ProcessingTransformersEnglish

The MLX MXFP4 quantized version of Gemma 3 27B IT QAT is a lightweight open - source multimodal model developed by Google. This model can simultaneously process text and image inputs and generate text outputs. It has a large context window of 128K and supports over 140 languages, making it suitable for various text generation and image understanding tasks.

MultimodalMlx

This is a 4-bit W4A16 quantized version based on the MiniMax M2 base model, quantized by @Qubitum of ModelCloud using the GPT-QModel tool. This model is specifically optimized for text generation tasks, significantly reducing the model size and inference resource requirements while maintaining good performance.

Natural Language ProcessingSafetensorsEnglish

Llama-4-Maverick-17B-128E-Instruct-NVFP4 is a multilingual large language model that has undergone FP4 quantization processing. It is based on the Meta-Llama-3.1 architecture and is specifically designed for business and research purposes. By quantizing the weights and activations to the FP4 data type, this model significantly reduces the disk space and GPU memory requirements while maintaining good performance.

Natural Language ProcessingSafetensorsMultiple Languages

This is a text generation model that is specifically fine-tuned based on the Google Gemma-3 model. After training, it can use external tools to generate step-by-step plans. This model is trained on the rewoo/planner_instruction_tuning_2k dataset and uses LoRA technology for efficient fine-tuning.

Natural Language ProcessingTransformers

This is a Lora image fusion tool based on the Qwen-Edit model, specifically designed for image-to-image conversion tasks. It can achieve excellent image fusion effects and is particularly good at image editing functions such as white model to material conversion.

Computer VisionDiffusers

This is the MXFP4_MOE quantized version of the Huihui GPT-OSS-120B-BF16-abliterated-v2 model, specifically optimized for text generation tasks to provide efficient text generation capabilities. The model reduces computational and storage requirements through quantization techniques while maintaining good performance.

Natural Language ProcessingGguf

This is the MXFP4_MOE quantized version of the Huihui-gpt-oss-20b-BF16-abliterated-v2 model, specifically optimized for text generation tasks. The model reduces the model size through quantization technology while maintaining good generation performance.

Natural Language ProcessingGguf

LongCat-Video is a basic video generation model with 13.6 billion parameters, which performs excellently in generation tasks such as text-to-video, image-to-video, and video frame continuation. It is particularly good at efficient and high-quality long video generation.

MultimodalDiffusersMultiple Languages