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 is a quantized version of TheDrummer's Cydonia-24B-v4.3 model. It uses llama.cpp for quantization processing, aiming to provide model files of different quantization types to meet the needs of different hardware and performance. It is a large language model with 24B parameters, mainly used for text generation tasks.

Natural Language ProcessingGguf

This is the GGUF quantization format version of the NousResearch/Hermes-4.3-36B model, quantized by MaziyarPanahi. GGUF is an efficient model format designed to optimize local deployment and inference performance, supporting multiple quantization levels (2-bit to 8-bit), and suitable for a wide range of text generation tasks.

Natural Language ProcessingGguf

OpenThinker - Agent - v1 is an open - source intelligent agent model that is subsequently trained based on Qwen3 - 8B. It is designed for terminal operations and software engineering tasks. It is first trained on a high - quality supervised fine - tuning dataset and then further optimized through reinforcement learning. It performs excellently in agent benchmark tests such as Terminal - Bench 2.0 and SWE - Bench.

Natural Language ProcessingTransformersOther

GigaChat3-10B-A1.8B is an efficient dialogue model in the GigaChat series. Based on the Mixture-of-Experts (MoE) architecture, it has a total of 10 billion parameters and 1.8 billion active parameters. It adopts the innovative Multi-head Latent Attention (MLA) and Multi-token Prediction (MTP) technologies, aiming to optimize inference throughput and generation speed. The model is trained on 20T tokens of diverse data and supports 10 languages including Chinese, suitable for dialogue scenarios requiring quick responses.

Natural Language ProcessingGgufMultiple Languages

Qwen3-4B-Hivemind-Instruct-NEO-MAX-Imatrix-GGUF is a large language model with 4B parameters for instruction fine-tuning based on the Qwen3 architecture. It uses NEO Imatrix and MAX quantization technologies and has the ability to process an ultra-long context of 256k. This model performs excellently in multiple benchmark tests and is a powerful general-purpose model.

Natural Language ProcessingGgufEnglish

This is the GGUF quantized version of the large language model Mistral-Large-3-675B-Instruct-2512 developed by Mistral AI. The original model has 675 billion parameters and is designed for instruction-following tasks. This project uses the llama.cpp tool and combines it with the imatrix calibration dataset to generate more than 20 different precision quantized model files from Q8_0 to IQ1_S, aiming to balance model performance, inference speed, and storage/memory usage, enabling it to run on a wider range of hardware.

Natural Language ProcessingGgufMultiple Languages

This is a video understanding model fine-tuned based on the VideoMAE-base architecture, specifically optimized for sign language recognition tasks. The model was trained on the WLASL dataset for 200 epochs, using the TimeSformer architecture to process video sequences.

Computer VisionTransformers

This is a video understanding model fine-tuned based on the VideoMAE-base architecture, specifically optimized for sign language recognition tasks. The model was trained on the WLASL100 dataset for 200 epochs and has the ability to recognize video actions.

Computer VisionTransformers

This is the quantized version of Qwen3-VL-32B-Thinking-heretic, using a new ablation method for quantization. Compared with traditional methods, it causes less damage to the base model. It offers multiple quantization precision versions and is suitable for running on devices with 24GB of VRAM, supporting image-to-text tasks.

MultimodalTransformersEnglish

This is a video understanding model fine-tuned on an unknown dataset based on the VideoMAE-base architecture, specifically designed for sign language recognition tasks. The model achieved an accuracy of 18.64% after 20 training epochs.

Computer VisionTransformers

Gheya-1 is a new-generation basic language model in the LES-IA-ETOILES ecosystem, with 202 million parameters. It is an upgraded version of the old Small-lamina series. This model is designed specifically for professional fine-tuning and has targeted training in the fields of artificial intelligence, professional language models, and biology.

Natural Language ProcessingTransformersFrench

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

Cecilia FT MS v1 is a Cuban language model fine-tuned based on Cecilia 2B v0.1, specifically optimized for Cuban Spanish to capture the nuances of Cuban language, culture, and society. This model supports Spanish and English and is mainly used for text generation tasks.

Natural Language ProcessingTransformersMultiple Languages

Olmo-3-7B-Instruct-AIO-GGUF is a GGUF quantized version based on the Olmo-3-7B-Instruct model developed by the Allen Institute for AI. This is an autoregressive language model with 7 billion parameters, trained on datasets such as Tulu 2 and UltraFeedback through supervised fine-tuning and direct preference optimization, and performs excellently in question-answering and instruction following.

Natural Language ProcessingTransformersEnglish

This is a video understanding model fine-tuned on an unknown dataset based on the MCG-NJU/videomae-base model. After 20 epochs of training, it achieved an accuracy of 13.31% on the evaluation set. This model is specifically optimized for video analysis tasks.

Computer VisionTransformers

Atom-Olmo3-7B is a language model fine-tuned based on Olmo-3-7B-Instruct, specifically designed for collaborative problem-solving and creative exploration. This model can provide thoughtful and structured analysis when dealing with complex problems, while maintaining an engaging conversation style. It has an Apache 2.0 open-source license.

Natural Language ProcessingTransformersEnglish

This is a LoRA model based on Qwen Image Edit 2509, specifically designed to convert input images into sketch comic artworks with exaggerated features. The model can create humorous and artistic comic images for human and animal subjects, highlighting facial features and characteristics.

Computer VisionDiffusersMultiple Languages

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

The Brazilian scope routing model is a fine-tuned micro large language model based on Gemma 3 270M, specifically designed for the Brazilian food delivery service system. It can serve as an extremely fast and lightweight intent classifier (router). This model is trained using Unsloth and converted to the GGUF format.

Natural Language ProcessingSafetensorsOther