#custom models

RAPTOR offers an advanced approach to language models with its recursive tree structure, improving the efficiency of information retrieval in large texts. It supports integration with custom models for summarization and question-answering, making it highly adaptable to different research requirements. The open-source nature encourages continuous enhancement through community contributions.

Oterm is a terminal client designed for managing multiple persistent chat sessions with customizable prompts and parameters. Users can integrate external tools to enhance model capabilities, using models from Ollama or their own. With multiple installation support and intuitive keyboard navigation, it offers a streamlined interface for chat management and session export.

ML.NET empowers .NET developers, even those with no previous machine learning experience, to effortlessly create, train, and deploy personalized machine learning models. This adaptable and open-source framework supports diverse scenarios including classification, forecasting, and anomaly detection. It enhances its extensibility by integrating with pre-existing TensorFlow and ONNX models. ML.NET seamlessly operates across Windows, Linux, and macOS, and integrates well with .NET Core and .NET Framework. With plentiful tutorials and comprehensive documentation, it makes model development both accessible and efficient for newcomers and experts alike. Ideal for industries ranging from finance to healthcare, ML.NET offers practical applications that streamline and advance business operations.

Offers a self-hosted, flexible solution compatible with OpenAI's stateful Assistant API. Facilitates seamless switching between OpenAI's official and custom APIs by adjusting the baseURL. Supports custom models and retrieval tools, while integrating with LangChain and LlamaIndex. Utilizes Postgres, Redis, and S3, granting developers complete control and sandboxed environments for assistant evaluations.