In the following video “0003 interacting with vision models part 1,” the presenter guides users on utilizing a novel language model (LLM) designed to process images as input instead of text. Users are advised to clear memory by ejecting existing models before selecting a vision model like Obsidian Vision, which demands 3.63 gigs of RAM. Unlike text-based counterparts, this innovative vision model accepts image inputs, which users can source from their computers or the internet for demonstration.
Video “0004 interacting with vision models part 2,” delves into utilizing vision models. They demonstrate this by capturing a screenshot from Pexels, describing its content, and utilizing the description as a prompt for a new image in LM Studio. Emphasizing the creative potential and uniqueness achievable through this method, the speaker urges viewers to explore diverse models and methods. Additionally, the video showcases the utilization of models like “M Vision” and encourages audience experimentation.
In this part of the presentation, video “0010 Using LM studio as backend API part 1”, the speaker illustrates the utilization of LM Studio as a RESTful API backend. This approach enables applications to interact with LM Studio without a user interface, facilitating functionalities such as sending prompts and receiving text or image descriptions seamlessly in the background. The speaker showcases the loading of a text prompt model in LM Studio and the establishment of a local inference server, operating on the same call signature as the OpenAI API, thus facilitating effortless backend server switching for developers. However, the speaker cautions about potential memory leaks in specific versions, advising that restarting the computer may be necessary for resolution.