This project provides a static quantized version of the Qwen-4B-Instruct-2507-Self-correct model, supporting tasks such as text generation, bias mitigation, and self-correction. Based on the Qwen-4B architecture, the model has undergone instruction fine-tuning and self-correction training, offering multiple quantized versions to meet different hardware requirements.

Natural Language ProcessingTransformersEnglish

This model is a violence detection model based on the VideoMAE architecture. After pre-training on the Kinetics dataset, it was fine-tuned for 92 rounds for the violence detection task. The model uses the Vision Transformer architecture and is specifically designed for video content analysis, capable of identifying violent behaviors in videos.

Computer VisionTransformers

MiniMax-M2-5bit is a 5-bit quantized version converted from the MiniMaxAI/MiniMax-M2 model, optimized specifically for the MLX framework. This model is a large language model that supports text generation tasks and is released under the MIT license.

Natural Language ProcessingMlx

This is a deep learning model based on the EfficientNet-B0 architecture, specifically designed for classifying the art styles of anime and visual novel images. The model can accurately identify 6 different anime art styles, including dark, flat, modern, cute, painting, and retro styles.

Computer VisionSafetensorsEnglish

OpenLLaMA 3B v2 is an open-source large language model based on the Transformer architecture, with 3 billion parameters. This model uses the MIT license and is mainly used for English text generation tasks, supporting various application scenarios such as chatbots.

Natural Language ProcessingPeftEnglish

This is a large language model with 70B parameters merged using the Linear DELLA method. Based on the Llama-3.1-Nemotron-lorablated-70B base model, it integrates multiple high-performance Llama-3.3-70B variant models, aiming to provide more powerful language understanding and generation capabilities.

Natural Language ProcessingTransformers

GLM-4.5-Air-AWQ is an 8-bit quantized version based on the GLM-4.5-Air base model, designed specifically for intelligent agents. It uses a hybrid reasoning mode, supports complex reasoning and instant response, and is released under the MIT open-source license.

Natural Language ProcessingTransformersMultiple Languages

GLM-4.5 is a foundation model designed for intelligent agents, integrating inference, coding, and intelligent agent capabilities. It has a total of 355 billion parameters and ranks 3rd in 12 industry-standard benchmark evaluations, scoring 63.2. It uses the MIT open-source license and can be used for commercial purposes and secondary development.

Natural Language ProcessingTransformersMultiple Languages

This is a static quantized version of the FBogaerts/NextCoder-7B-Finetuned model, specifically optimized for code generation and text generation tasks, providing a more efficient inference solution. The model supports English and is released under the MIT license.

Natural Language ProcessingTransformersEnglish

GLM-4.5-Air-AWQ is a text generation model that performs 4-bit AWQ quantization based on the zai-org/GLM-4.5-Air base model. It is specifically designed for intelligent agent applications and performs excellently in reasoning, coding, and intelligent agent capabilities. It uses the MIT open-source license.

Natural Language ProcessingTransformersMultiple Languages

GLM-4.5-Air is an efficient language model developed by zai-org. It has been optimized by the MLX team with 8-bit quantization and specifically optimized for Apple Silicon chips. This model is based on the transformers architecture, supports text generation tasks, and is released under the MIT license.

Natural Language ProcessingTransformersMultiple Languages

M-Hood is a series of models specifically designed to mitigate the hallucination phenomenon in object detection. Through novel fine-tuning strategies and a revised benchmark dataset, it significantly reduces false alarms on out-of-distribution data and enhances the safety and reliability of object detection systems.

Computer VisionPytorch

The FLUX.1-Kontext-dev version processed by Nunchaku quantization can edit images according to text instructions, optimizing inference efficiency while minimizing performance loss.

Computer VisionDiffusersEnglish

A Nunchaku quantized version based on Shuttle Jaguar, designed to generate high-quality images according to text prompts, optimizing efficient inference and minimizing performance loss.

Computer VisionDiffusersEnglish

The Nunchaku quantized version of FLUX.1-Fill-dev, capable of filling regions in existing images based on text descriptions, achieving efficient inference through quantization processing.

Computer VisionDiffusersEnglish

The Nunchaku-quantized FLUX.1-schnell model is used to generate high-quality images based on text prompts and has been optimized for efficient inference.

Computer VisionDiffusersEnglish

The quantized version of FLUX.1-Depth-dev provided by the Nunchaku team can generate images based on text descriptions while preserving the structure of the input image, optimizing the inference efficiency.

Computer VisionDiffusersEnglish

The Nunchaku quantized version of FLUX.1-Canny-dev, capable of generating images based on text descriptions while following the structure of the given input image.

Computer VisionDiffusersEnglish

An image segmentation model fine-tuned based on nvidia/mit-b3, performing excellently on the NICOPOI-9/morphpad_coord_hgo_512_4class_v2 dataset

Computer VisionTransformers

SegFormer image segmentation model based on NVIDIA MIT-B1 architecture, fine-tuned on specific datasets, excels in high-precision image segmentation tasks

Computer VisionTransformers