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

OpenMMReasoner is a fully transparent two-stage multimodal reasoning solution that covers supervised fine-tuning (SFT) and reinforcement learning (RL). By carefully constructing high-quality datasets, this solution surpasses powerful baseline models in multiple multimodal reasoning benchmark tests, laying a solid empirical foundation for future large-scale multimodal reasoning research.

MultimodalTransformers

Granite-4.0-1B is a lightweight instruction model developed by IBM, fine-tuned based on Granite-4.0-1B-Base. This model combines open-source instruction datasets and internal synthetic datasets, and is developed using techniques such as supervised fine-tuning, reinforcement learning, and model merging. It is suitable for device-side deployment and research use cases.

Natural Language ProcessingTransformers

Granite-4.0-350M is a lightweight instruction model developed by IBM, fine-tuned based on Granite-4.0-350M-Base. This model combines open-source instruction datasets and internal synthetic datasets, and is developed using technologies such as supervised fine-tuning, reinforcement learning, and model fusion. It has powerful instruction-following capabilities and is particularly suitable for device-side deployment and research scenarios.

Natural Language ProcessingTransformers

OLGA is an online reinforcement learning positioning agent built on Qwen3-VL-30B-A3B-Instruct, using a mixture-of-experts model with 3.3 billion active parameters. It is trained through a new data recipe that combines existing datasets, new data collection, automatic filtering, and online reinforcement learning, achieving advanced positioning performance among open-source models.

MultimodalTransformersEnglish

Granite-4.0-350M is a lightweight instruction model developed by IBM, fine-tuned based on Granite-4.0-350M-Base. This model combines open-source instruction datasets and internal synthetic datasets, and is developed using supervised fine-tuning, reinforcement learning, and model merging techniques. It has powerful instruction-following capabilities and tool invocation functions.

Natural Language ProcessingTransformers

Granite-4.0-1B is a lightweight instruction model developed by IBM. It is fine-tuned based on Granite-4.0-1B-Base, combining open-source instruction datasets and internal synthetic datasets, and developed using supervised fine-tuning, reinforcement learning, and model merging techniques.

Natural Language ProcessingTransformers

Granite-4.0-H-350M is a lightweight instruction model developed by IBM, fine-tuned based on Granite-4.0-H-350M-Base. This model combines open-source instruction datasets and internal synthetic datasets, and is developed using various technologies such as supervised fine-tuning, reinforcement learning, and model merging. It has powerful instruction-following capabilities and multilingual support.

Natural Language ProcessingTransformers

Granite-4.0-H-Small is a long-context instruction model developed by IBM with 32 billion parameters, fine-tuned based on Granite-4.0-H-Small-Base. This model combines open-source instruction datasets and internal synthetic datasets, and uses techniques such as supervised fine-tuning, reinforcement learning alignment, and model merging. It has significantly improved instruction following and tool invocation capabilities, and is particularly suitable for enterprise-level applications.

Natural Language ProcessingTransformers

Granite-4.0-H-Micro is a 3-billion parameter long-context instruction model developed by IBM, fine-tuned from Granite-4.0-H-Micro-Base. This model combines open-source instruction datasets and internal synthetic datasets, and is developed using techniques such as supervised fine-tuning, reinforcement learning alignment, and model merging. It has a structured chat format and performs excellently in instruction following and tool invocation capabilities.

Natural Language ProcessingTransformers

Granite-4.0-H-Tiny is a long-context instruction model developed by IBM with 7 billion parameters, fine-tuned based on Granite-4.0-H-Tiny-Base. This model combines open-source instruction datasets and internal synthetic datasets, and is developed using techniques such as supervised fine-tuning, reinforcement learning alignment, and model merging. It has enhanced instruction-following and tool invocation capabilities, making it particularly suitable for enterprise-level applications.

Natural Language ProcessingTransformers

Granite-4.0-Micro is a long-context instruction model with 3 billion parameters developed by IBM, fine-tuned based on Granite-4.0-Micro-Base. This model combines open-source instruction datasets and internal synthetic datasets, and is developed using technologies such as supervised fine-tuning, reinforcement learning alignment, and model merging. It has enhanced instruction-following and tool invocation capabilities, and is particularly suitable for enterprise-level applications.

Natural Language ProcessingTransformers

Granite-4.0-H-Small is a long-context instruction model with 32 billion parameters developed by IBM, fine-tuned based on Granite-4.0-H-Small-Base. This model combines open-source instruction datasets and internal synthetic datasets and is developed using techniques such as supervised fine-tuning, reinforcement learning alignment, and model merging, with significant improvements in instruction following and tool invocation capabilities.

Natural Language ProcessingTransformers

Granite-4.0-H-Tiny is a 7-billion parameter long context instruction model developed by IBM, fine-tuned from Granite-4.0-H-Tiny-Base. This model is trained by combining open-source instruction datasets and internal synthetic datasets, and has the ability to provide professional, accurate, and secure responses. It supports multiple languages and tool invocation, and is suitable for enterprise-level applications.

Natural Language ProcessingTransformers

Instinct is the most advanced open next-editing model launched by Continue. Based on the Qwen2.5-Coder-7B model, it has been robustly fine-tuned on real-world code editing datasets and can intelligently predict the developer's next operation.

Natural Language ProcessingTransformers

Granite-3.1-8B-Instruct is a long-context instruction model with 8 billion parameters, fine-tuned from Granite-3.1-8B-Base. It combines open-source instruction datasets and internal synthetic datasets, and is specifically designed to effectively solve long-context problems, supporting multiple languages and various AI tasks.

Natural Language ProcessingTransformers

SESAME is an open-source multimodal model, fine-tuned on various instruction-based image localization (segmentation) datasets based on the LLaVA model.

MultimodalTransformers

Phi-4 Reasoning is a cutting-edge open-weight reasoning model based on Phi-4, fine-tuned with supervised chain-of-thought trajectory datasets and trained via reinforcement learning, specializing in mathematics, science, and programming skills.

Natural Language ProcessingTransformersMultiple Languages

Granite-3.2-2B-Instruct is a 2-billion-parameter long-context AI model specifically fine-tuned for reasoning capabilities. Built upon Granite-3.1-2B-Instruct, it was trained using a mix of permissively licensed open-source datasets and internally generated synthetic data to enhance performance on reasoning tasks.

Natural Language ProcessingGguf

An Indian English accent speech recognition model fine-tuned based on OpenAI Whisper-large-v3-turbo, achieving a 7.99% word error rate on Indian English accent datasets

Audio ProcessingTransformersEnglish