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-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-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

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

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 large language model for risk detection developed by IBM Research, fine-tuned based on the Granite 3.2 3 billion parameter model, specifically designed to detect various risks in prompts and responses, providing security protection for enterprise applications.

Natural Language ProcessingTransformers