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 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

This is a quantized version of the kldzj/gpt-oss-120b-heretic model, quantized using llamacpp. It offers a variety of quantization types, including special formats such as BF16, Q8_0, and MXFP4_MOE, significantly improving the model's operating efficiency.

Natural Language ProcessingGguf

This is a quantized version of the p-e-w/gpt-oss-20b-heretic model, quantized using the imatrix technology of llama.cpp. The model is a large language model with 20 billion parameters, offering a variety of quantization options ranging from high quality to low quality, with file sizes from 41.86GB to 11.52GB, suitable for different hardware conditions.

Natural Language ProcessingGguf

This model is based on Qwen3-4B-Thinking-2507 and fine-tuned on 1000 examples of GPT-5-Codex. It focuses on text generation tasks and uses Unsloth technology to achieve a 2-fold increase in training speed.

Natural Language ProcessingTransformersEnglish

gpt-oss-safeguard-20b is a security inference model fine-tuned based on GPT-OSS-20b, specifically designed for input-output filtering of large language models, online content annotation, and offline annotation for trust and security use cases. This model uses the Apache 2.0 license and supports custom policies and a transparent decision-making process.

Natural Language ProcessingTransformers

gpt-oss-safeguard-120b is a security reasoning model built by OpenAI based on gpt-oss, with 117 billion parameters (5.1 billion of which are active parameters). This model is specifically designed for security use cases and can classify text content and perform basic security tasks according to the provided security policies.

Natural Language ProcessingTransformers

GPT-OSS-Safeguard-20B is a security reasoning model with 21 billion parameters built on GPT-OSS, specifically optimized for security-related text content classification and filtering tasks. This model supports custom security policies, provides a transparent reasoning process, and is suitable for security use cases such as input-output filtering of large language models and online content annotation.

Natural Language ProcessingTransformers

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

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 an 8-bit quantized version of the OpenAI GPT-OSS-20B model in MLX format, converted using mlx-lm 0.28.2 and optimized for Apple Silicon, providing efficient text generation capabilities.

Natural Language ProcessingTransformers

This is a content fidelity model based on the unsloth/gpt-oss-20b-BF16 base model, specifically designed to convert raw text into a concise and clear Markdown format. The model uses the Apache 2.0 license and mainly supports English text processing.

Natural Language ProcessingTransformersEnglish

NVIDIA GPT-OSS-120B Eagle3 is an optimized version based on the OpenAI gpt-oss-120b model. It adopts the Mixture of Experts (MoE) architecture, with a total of 120 billion parameters and 5 billion active parameters. This model supports both commercial and non-commercial use and is suitable for text generation tasks, especially for the development of AI Agent systems, chatbots, and other applications.

Natural Language ProcessingSafetensors

This is the GGUF format version of the GPT-OSS 20B model based on WeOpenML, which first implements the innovative MoE CPU expert offloading technology. This technology achieves a 99.9% reduction in video memory while maintaining the full generation quality, and only requires 2MB of video memory to run a 20 billion parameter mixture of experts model.

Natural Language ProcessingSafetensorsMultiple Languages

This project distills the inference ability of GPT into the Llama-3.1-8B model through an innovative two-stage training process. First, knowledge distillation and format alignment are performed through supervised fine-tuning, and then reinforcement learning is used to encourage the model to independently explore and optimize inference strategies, focusing on breakthroughs in the field of mathematical reasoning.

Natural Language ProcessingTransformersMultiple Languages

GLM-4.6-GPTQ-Int4-Int8Mix is a quantized version based on the zai-org/GLM-4.6 base model, which performs excellently in text generation tasks. This model achieves optimization of the model size through GPTQ quantization technology while maintaining good performance.

Natural Language ProcessingTransformers

WEBGEN DEVSTRAL IMAGES is an AI model focused on web page generation. It can generate single-page web pages using HTML, CSS, JS, and Tailwind technologies. This project is trained based on custom templates and uses the supervised fine-tuning method, training with a dataset generated by GPT-OSS-120B.

Natural Language ProcessingTransformersEnglish

This model is based on GPT-OSS-20B and fine-tuned using the Unsloth reinforcement learning framework. The aim is to optimize inference efficiency and reduce vulnerabilities that occur during reinforcement learning from human feedback (RLHF) training. The fine-tuning process focuses on the robustness and efficiency of alignment, ensuring that the model maintains inference depth without incurring excessive computational overhead.

Natural Language ProcessingTransformersEnglish

This is the GGUF quantized version of the Qwen/Qwen3-Coder-30B-A3B-Instruct language model, optimized for local inference and supporting frameworks such as llama.cpp, LM Studio, OpenWebUI, and GPT4All. This model is a code generation and programming assistant model with a parameter scale of 30B.

Natural Language ProcessingGgufMultiple Languages

This is the GGUF quantized version of the Qwen/Qwen3-14B language model, with 14 billion parameters. It has deep reasoning ability, research-level accuracy, and an autonomous workflow. After conversion, it can be used in local inference frameworks such as llama.cpp, LM Studio, OpenWebUI, and GPT4All.

Natural Language ProcessingGgufMultiple Languages