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A Node-RED node that interacts with OpenAI machine learning models like "ChatGPT". Install with the built-in Node-RED Palette manager. When editing the properties of the node, to get your OPENAI_API_KEY log in to ChatGPT. Create a new secret key" then copy and paste the "API key" into the node API_KEY property value. msg.payload should be a well-written prompt that provides enough information for the model to know what you want and how it should respond. Its success generally depends on the complexity of the task and quality of your prompt. A good rule of thumb is to think about how you would write a word problem for a middle schooler to solve. msg.payload should be a well-written prompt that provides enough information for the model to know what you want and how it should respond.

(Golang) Go bindings for the Hugging Face Inference API. Directly call any model available in the Model Hub. An API key is required for authorized access. To get one, create a Hugging Face profile.

...Built on .NET 9 and Avalonia UI, it provides a sleek, high-performance workspace for crafting perfect prompts for Stable Diffusion, FLUX, Midjourney, and other generative AI models. ## Key Capabilities - Multi-model tagging: Specialized generators for Stable Diffusion (tags), FLUX/Midjourney (prose), and custom LLM workflows - Multimodal vision: Extract metadata or generate descriptive captions directly from images - Contextual chat assistant: Persistent, coding-capable AI helper with real-time provider features - Persona system: Create custom identities with dynamic input injection and role-based templates - Chat rules: Modular behavioral controls (Concise, Detailed, etc.) with CRUD interface - Agent orchestrator: Centralized management of API keys, endpoints, and model parameters

...It leverages the use of transformers on many tasks with different models in this all-in-one framework. All you need is a little change of config. You can use tfkit for model training and evaluation with tfkit-train and tfkit-eval. The key to combine different task together is to make different task with same data format. All data will be in csv format - tfkit will use csv for all task, normally it will have two columns, first columns is the input of models, the second column is the output of models. Plane text with no tokenization - there is no need to tokenize text before training, or do re-calculating for tokenization, tfkit will handle it for you. ...