Viso Suite vs OpenHermes-13B

Viso Suite is an enterprise visual intelligence platform from viso.ai for building, deploying, and operating computer vision applications across cameras, sites, and industries. It connects existing camera feeds, edge devices, AI models, and business systems so operations teams can turn video into real-time alerts, dashboards, and automated workflows without stitching together separate point tools.

The platform is built around Visual General Intelligence (VGI), viso.ai's approach to scene understanding that lets teams describe what they want a camera to detect and build governed vision applications through a visual no-code studio. Viso Suite covers the full lifecycle: data collection and model training, application development, multi-site deployment, and ongoing operations with monitoring, security, and compliance controls.

Viso Suite targets complex organizations running physical operations in manufacturing, construction, transportation, retail, agriculture, healthcare, and smart city environments. Customers use it for safety monitoring, quality inspection, PPE compliance, restricted-zone alerts, crowd analytics, and other mission-critical vision use cases that need to scale across thousands of cameras and hundreds of sites.

OpenHermes-13B is a fine-tuned language model built on a fully open-source dataset of 242,000 entries, primarily generated by GPT-4 and curated from various open AI datasets. It was developed to improve text generation by removing AI disclaimers and refusals, aiming for clearer and more direct communication outputs. The model incorporates data from multiple contributors, including Teknium, WizardLM Team, Microsoft, and others, ensuring a diverse and comprehensive training base.

This model is designed for developers and researchers who want an open-source alternative for advanced text generation tasks without the typical AI safety disclaimers. It supports integration with popular machine learning libraries like Transformers and can be deployed locally or via Docker, making it accessible for experimentation and production use.

OpenHermes-13B offers transparency in its training process, with public access to its WANDB project logs and detailed benchmark results. It shows competitive performance on benchmarks like GPT4All and BigBench, with slight improvements over similar models, although it has some trade-offs in AGI-Eval scores.

Technically, it was trained using multi-GPU setups with Adam optimizer and cosine learning rate scheduling, emphasizing reproducibility and open collaboration. The model is compatible with various inference tools and quantizations, allowing use in environments like llama.cpp and LM Studio.

Overall, OpenHermes-13B provides a valuable resource for those seeking a powerful, open-source language model fine-tuned for instruction following and text generation without restrictive AI disclaimers, suitable for research, development, and deployment in diverse AI applications.