Average Model Cost: $0.0315
Number of Runs: 785,402
Models by this creator
musicgen is a text-to-audio model that is capable of generating music based on a given prompt or melody. It uses artificial intelligence algorithms to analyze and understand the musical attributes of the input and then generates a composition that is in line with the style and genre specified. The model allows users to input text prompts or musical notation and receive a corresponding music composition as an output. This can be useful for musicians, composers, and music enthusiasts who are looking for inspiration or want to explore new musical ideas.
musicgen is a model that can generate music from a given prompt or melody. It uses deep learning techniques to learn the patterns and structures of music and then generate original compositions based on the provided input. The model has been trained on a large dataset of music and can generate music in various genres and styles. It can be used to generate music for various purposes such as background music, jingles, or original compositions.
The instructblip-vicuna13b model is a multi-modal model that has been fine-tuned for instruction generation. It combines the strengths of the BLIP-2 and Vicuna-13B models to generate more accurate and relevant instructions in response to user queries. This model is trained using a combination of text and image inputs to provide more contextual understanding and produce higher-quality instructions.
MPLUG-OWL is an instruction-tuned multimodal large language model that generates text by analyzing user-provided prompts and images. It is designed to understand and process both textual and visual inputs, and then generate relevant and coherent text based on the given instructions. The model can be trained on various tasks and domains, allowing for a wide range of applications such as image captioning, dialog systems, and creative writing. The goal of MPLUG-OWL is to assist users in generating high-quality and contextually-appropriate text by leveraging the power of both language and visual information.