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 is a cross-encoder model fine-tuned on the LangCache sentence pair dataset using the sentence-transformers library, based on the Alibaba-NLP/gte-reranker-modernbert-base model. It is specifically designed to calculate the semantic similarity score between text pairs, aiming to provide efficient text matching and reordering capabilities for the LangCache semantic cache system.

Natural Language ProcessingSentence-transformersEnglish

This is a cross-encoder model fine-tuned by Redis, specifically designed for sentence pair re-ranking in semantic caching scenarios. It is based on the gte-reranker-modernbert-base model of Alibaba-NLP and trained on the LangCache sentence pair dataset. It can efficiently calculate the semantic similarity score between two texts to determine whether they express the same or similar meanings.

Natural Language ProcessingSentence-transformersEnglish

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

This is a CrossEncoder model developed by Redis and fine-tuned for the LangCache semantic caching task. It is based on the mature `cross-encoder/ms-marco-MiniLM-L6-v2` model and trained on a dataset of over 1 million LangCache sentence pairs. It is specifically designed to calculate the semantic relevance score between two texts to optimize the cache hit rate.

Natural Language ProcessingSentence-transformersEnglish

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 an object detection model fine-tuned on a custom dataset based on the YOLOv12 Medium architecture. This model is specifically designed to efficiently and accurately detect drone targets in images or videos, providing support for computer vision applications.

This is a lightweight text generation model based on the Meta LLaMA 3 8B model. It is efficiently fine-tuned using the Unsloth framework and converted to the GGUF format. The model is optimized for the Alpaca dataset, suitable for local deployment and inference, especially in resource-constrained environments.

Natural Language ProcessingGguf

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 video understanding model based on the VideoMAE architecture. It was pre-trained on the Kinetics dataset and fine-tuned on an unknown dataset that may be related to sign language recognition. The model achieved an accuracy of 78.11% on the evaluation set and is suitable for video classification tasks.

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 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 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

This is a video understanding model based on the VideoMAE architecture. It has been fine-tuned on the basis of pre-training on the Kinetics dataset and is specifically designed for sign language recognition tasks. The model's performance on the evaluation set needs improvement, with an accuracy of 0.0010.

Computer VisionTransformers

This model is obtained through distillation training using the high-reasoning-difficulty Gemini 3 Pro preview dataset based on the Qwen3-4B-Thinking-2507 base model. It focuses on improving complex reasoning abilities in the fields of coding and science. Through training with specific datasets, it aims to efficiently transfer the reasoning abilities of large models (such as Gemini 3 Pro) to a smaller-scale model.

Natural Language ProcessingTransformers

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

Zenyx_114M-Tiny-Edu-Instruct is an experimental small instruction fine-tuned language model with approximately 114 million parameters. It is built on the TinyEdu-50M base model, pre-trained on the FineWeb-Edu dataset, and fine-tuned on a mixed dataset of OpenHermes-2.5 and CodeFeedback-Filtered. This model aims to explore the limits of instruction fine-tuning under a minimal architecture and verify that the loss converges to approximately 1.04.

Natural Language ProcessingPytorchEnglish

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

This is a quantized version of the MiroThinker-v1.0-8B model developed by miromind-ai. It is quantized using specific tools and datasets, providing users with files of various quantization types to choose from and can run in different environments to meet different performance and quality requirements.

Natural Language ProcessingGgufEnglish

This is a quantized version of TheDrummer's Snowpiercer-15B-v4 model, quantized using llama.cpp and imatrix technology. This version offers multiple quantization levels from BF16 to IQ2_S, meeting the needs of users with different hardware conditions and performance requirements. The model is calibrated and optimized using a specific dataset and supports the online repackaging function to improve performance on ARM and AVX hardware.

Natural Language ProcessingGguf