Z-Image is an efficient basic image generation model with 6 billion parameters, specifically designed to address the efficiency and quality issues in the field of image generation. Its distilled version, Z-Image-Turbo, can reach or exceed the leading competitors with only 8 function evaluations. It can achieve sub-second inference latency on enterprise-level H800 GPUs and run on consumer-grade devices with 16G VRAM.

Computer VisionDiffusersMultiple Languages

RealRobot_chatbot_llm is a professional product artificial intelligence model based on the Gemma3n architecture, specifically fine-tuned on the proprietary dataset of the RealRobot product catalog. This model can quickly create an accurate, cost-effective, and deployable professional language model based on the enterprise's own product data, providing highly specialized product Q&A services.

Natural Language ProcessingGgufMultiple Languages

IBM Granite-4.0-H-Tiny is a hybrid Mamba-2/Transformer model optimized for Apple silicon chips, using 3-bit quantization technology, and is designed for long context, efficient inference, and enterprise use. This model combines the Mamba-2 architecture with the Mixture of Experts (MoE) technology, significantly reducing memory usage while maintaining expressiveness.

Natural Language ProcessingMlxMultiple Languages

Apriel-1.5-15b-Thinker is a 15-billion parameter multimodal reasoning model developed by ServiceNow. It has the ability to reason with text and images, and its performance can rival models 10 times larger. It scored 52 points on the manual analysis index and performed excellently in enterprise domain benchmark tests.

MultimodalTransformers

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-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-Micro is a 3-billion-parameter long-context instruction model developed by IBM, fine-tuned from Granite-4.0-H-Micro-Base. This model is trained by combining an open-source instruction dataset and an internal synthetic dataset, and it has enhanced instruction-following and tool call capabilities, making it particularly suitable for enterprise-level applications.

Natural Language ProcessingTransformers

Granite-4.0-Micro is a 3-billion-parameter instruction fine-tuned model in the Granite 4.0 series developed by IBM. It is optimized for long context reasoning, efficient reasoning, and enterprise-level functions, demonstrating powerful performance while maintaining a relatively small scale.

Natural Language ProcessingGguf

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

Granite-4.0-H-Small is a long-context instruction model with 32 billion parameters developed by IBM, specifically designed for enterprise-level AI assistants and supporting multiple languages and professional application scenarios.

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 is trained by combining an open-source instruction dataset and an internal synthetic dataset, with multilingual support and enhanced tool invocation capabilities, and performs excellently in enterprise applications.

Natural Language ProcessingTransformers

CAI-20B is a specialized marketing strategy expert model fine-tuned based on the OpenAI GPT-OSS-20B model, focusing on providing marketing strategies, performance marketing, and business growth advice. This model can provide actionable marketing advice, campaign strategies, budget allocation suggestions, and growth strategies for enterprises of different sizes.

Natural Language ProcessingTransformersEnglish

AFM-4.5B is a 4.5-billion parameter instruction-tuned model developed by Arcee.ai, designed specifically for enterprise-level applications. It supports multilingual processing and demonstrates excellent performance in various deployment environments from the cloud to the edge.

Natural Language ProcessingTransformersMultiple Languages

Cohere Labs Command A Vision is an enterprise-level vision-language model with 112 billion parameters, optimized for image understanding tasks. It has low computational requirements while maintaining high performance. The model supports multilingual input and output and can handle multimodal input of images and text.

MultimodalTransformersMultiple Languages

This is a Transformer model specifically designed for disease entity recognition. It is based on the xlm-roberta-base architecture and fine-tuned on the NCBI disease dataset. The model can accurately identify disease entities in biomedical texts and supports the recognition of two entity types, B-Disease and I-Disease, providing high-precision entity extraction capabilities for enterprise-level medical applications.

Natural Language ProcessingTransformersEnglish

This is a high-precision model specifically designed for biomedical entity recognition. It is fine-tuned based on the deberta-v3-base architecture and can identify biomedical entities such as proteins, DNA, RNA, cell lines, and cell types, providing an enterprise-level solution for clinical and research applications.

Natural Language ProcessingTransformersMultiple Languages

A.X 3.1 Light is a lightweight large language model (LLM) optimized for Korean understanding and enterprise deployment, fully independently developed by SKT.

Natural Language ProcessingTransformersMultiple Languages

Granite Guardian 3.2 5B is an AI risk detection model developed by IBM, specifically designed to detect various security risks in prompts and responses. Based on the IBM AI Risk Atlas, this model can identify multiple risk types such as harm, social bias, jailbreaking, and violence, and is an important tool for enterprise-level AI security monitoring.

Natural Language ProcessingTransformers

A.X 4.0 Light is a lightweight large language model developed by SKT AI Model Lab, built on Qwen2.5 and optimized for Korean understanding and enterprise deployment.

Natural Language ProcessingTransformersMultiple Languages