This is an open-source large language model with 20 billion parameters focusing on the field of network security. It is based on the GPT-OSS architecture and fine-tuned by integrating multiple network security instruction datasets. The model provides various quantization versions, which are convenient for deployment in resource-constrained environments and can be used to generate network security-related texts, codes, and analysis reports.

Natural Language ProcessingTransformersEnglish

This project provides a text generation model fine-tuned based on the Microsoft Phi-3.5-mini-instruct model, which has been converted to the GGUF format and is suitable for the llama.cpp inference framework. The model was fine-tuned on the philschmid/guanaco-sharegpt-style dataset, optimizing its instruction-following and dialogue capabilities, and is suitable for efficient text generation tasks in resource-constrained environments.

Natural Language ProcessingGguf

This is a quantized version of the squ11z1's Hypnos-i1-8B model, which is quantized using the llama.cpp tool and a specific dataset. The model is based on the Llama-3 architecture, offering multiple quantization type options and supporting text generation tasks. It is suitable for various application scenarios such as inference and dialogue.

Natural Language ProcessingGgufEnglish

This is an experimental quantized large language model that uses the Deckard (qx) quantization method with 3-bit quantization for the embedding layer. The model is reconstructed using the Norm-Preserving Bi-Projection Elimination (NPBA) technique, which not only removes the security restriction mechanism but also enhances the model's cognitive depth and reasoning ability.

Natural Language ProcessingMlx

This repository contains a 4-step distilled model optimized for HunyuanVideo-1.5, which can achieve ultra-fast 4-step inference without using CFG (Classifier-Free Guidance), significantly reducing the generation time while maintaining high-quality video output.

MultimodalDiffusers

This is a video understanding model based on the VideoMAE-base architecture, which has been fine-tuned for 20 epochs on an unknown dataset. The model's performance on the evaluation set is limited, with an accuracy of 0.0041 and a loss value of 7.7839.

Computer VisionTransformers

Gemma 3 is a lightweight open-source multimodal model launched by Google. This version is an instruction-tuned quantization-aware training model with 12B parameters, which has been converted to the MXFP4 format of the MLX framework. It supports text and image input and generates text output, with a 128K context window and support for over 140 languages.

MultimodalMlx

This is a quantized version of ai-sage's GigaChat3-10B-A1.8B model, using the imatrix quantization technology of llama.cpp, which can run more efficiently under different hardware conditions. The model supports Russian and English and is mainly used for text generation tasks.

Natural Language ProcessingGgufMultiple Languages

This project provides a quantized GGUF version of Tencent's Hunyuan Video 1.5 text-to-video model, which supports 480P video generation tasks. It includes both distilled and full models and can be used in conjunction with tools such as ComfyUI-GGUF.

MultimodalGguf

This is a housing condition classifier fine-tuned based on the Google ViT-base model, which can classify housing images into four categories: good, unknown, dilapidated, and medium. The model was trained on a dataset of 935 housing images, and the accuracy on the validation set reached 81.2%.

Computer VisionTransformersEnglish

A quantized model optimized for HunyuanVideo-1.5, used in conjunction with the LightX2V framework, which significantly reduces memory usage while maintaining high-quality video generation performance.

MultimodalDiffusers

This is the quantized version of kldzj's GPT-OSS-120B-Heretic-v2 model, which is quantized using the imatrix technology of llama.cpp. The model significantly reduces the storage and computing resource requirements through quantization technology while maintaining good model performance, facilitating deployment and operation on various devices.

Natural Language ProcessingGguf

SAM 3 is the third-generation promptable segmentation foundation model launched by Meta, which uniformly supports image and video segmentation tasks. Compared with its predecessor SAM 2, it introduces the ability of open vocabulary concept segmentation, can handle a large number of text prompts, and achieves 75-80% of human performance in the SA-CO benchmark test.

Computer VisionTransformersEnglish

This is the quantized version of the ai-sage/GigaChat3-10B-A1.8B model, using the GGUF format and requiring support from a specific llama.cpp branch. The model adopts a Mixture of Experts architecture, with a total of approximately 11.8 billion parameters, of which approximately 1.8 billion are activated parameters.

Natural Language ProcessingGgufMultiple Languages

GigaChat3-10B-A1.8B is a dialogue model in the GigaChat series, based on the Mixture of Experts (MoE) architecture, with a total of 10 billion parameters, of which 1.8 billion are active parameters. This model uses multi-head latent attention and multi-token prediction technologies, supports a long context of 256,000 tokens, and performs excellently in multilingual dialogue and reasoning tasks.

Natural Language ProcessingSafetensorsMultiple Languages

This project provides a quantized version of the cerebras/MiniMax-M2-REAP-172B-A10B model, aiming to make knowledge accessible to the public. This is a large language model with 172 billion parameters, which has been optimized and quantized to reduce deployment costs and improve inference efficiency.

Natural Language ProcessingGguf

Olmo 3 is a series of language models developed by the Allen Institute for AI (Ai2), including two specifications of 7B and 32B, with two variants of Instruct and Think. This model is based on the Transformer architecture and has the ability of long-chain thinking, which can effectively improve the performance of reasoning tasks such as mathematics and coding.

Natural Language ProcessingTransformersEnglish

SAM3 is a concept-based arbitrary segmentation model that can generate precise image segmentation masks based on input prompt information such as points and boxes. This version is the ONNX format of the SAM3 tracker model, which can run efficiently in the browser environment through the Transformers.js library.

Computer VisionTransformers

This is the MXFP4_MOE quantized version of the MiniMax-M2-REAP-172B-A10B model, which is a memory-efficient compressed model. Through the REAP (Routing-Weighted Expert Activation Pruning) method, the model is compressed from 230B parameters to 172B parameters while maintaining performance, with a 25% reduction in size. It is suitable for resource-constrained environments, local deployment, and academic research.

Natural Language ProcessingGguf

Olmo 3 7B RL-Zero Math is a 7-billion-parameter language model developed by Allen AI, specifically optimized for mathematical reasoning tasks. It is trained on mathematical datasets using the RL-Zero reinforcement learning method, which can effectively improve mathematical reasoning ability.

Natural Language ProcessingTransformersEnglish