📄️ Generation endpoints
To run inference with takeoff, simply POST a JSON payload containing text (the prompt) and parameters to the REST API endpoint, which will return a JSON response containing the generated text.
📄️ Embedding endpoints
Embedding with Takeoff requires making a request to the single embedding endpoint: embed.
📄️ Classification endpoints
Using classification / reranking / question answering models with Takeoff requires making a request to the classification endpoint: classify.
📄️ Image to Text endpoints
Takeoff supports image to text generation using multi-modal generative models like Llava. Models like Llava are trained to be able to describe and interact with images provided to them, and can be used for question-asnwering over images, OCR, and image captioning.
📄️ API Models
You can access models from API-providers like OpenAI through Takeoff. This makes it possible to build applications that seamlessly use open-source, self-hosted LLMs and proprietary, closed-source models like GPT4.
📄️ Using the GUI
Takeoff is packaged with some easy-to-use demo interfaces to help you quickly test out a model. There are two interfaces available: Chat - similar to that of OpenAI chat (chatGPT) and Playground - similar to that of OpenAI completions.
📄️ Generating JSON/Regex
Takeoff allows you to generate outputs which match a given regular expression or JSON Schema. This lets you output data with a consistent format, adhering to any type requirements.
📄️ OpenAI Compatibility API
Takeoff has an integrated API layer to provide compatibility with OpenAI's Chat Completion API. This means that developers can now use OpenAI's existing client libraries or minimally adapt existing codebases to interact seamlessly with Takeoff via the API layer.