OpenHermes-13B vs Civitai

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.

Civitai is a platform that makes it easy for people to share and discover resources for creating AI art. Our users can upload and share custom models that they've trained using their own data, or browse and download models created by other users. These models can then be used with AI art software to generate unique works of art.

Cool, what's a "Model?" Put simply, a "model" refers to a machine learning algorithm or set of algorithms that have been trained to generate art or media in a particular style. This can include images, music, video, or other types of media.

To create a model for generating art, a dataset of examples in the desired style is first collected and used to train the model. The model is then able to generate new art by learning patterns and characteristics from the examples it was trained on. The resulting art is not an exact copy of any of the examples in the training dataset, but rather a new piece of art that is influenced by the style of the training examples.

Models can be trained to generate a wide range of styles, from photorealistic images to abstract patterns, and can be used to create art that is difficult or time-consuming for humans to produce manually.