Run:ai offers a platform to accelerate AI development, optimize GPU utilization, and manage AI workloads. It is designed for GPUs, offers CLI & GUI interfaces, and supports various AI tools & frameworks.
The future of iOS apps might be services that just tie into Apple Intelligence, with little to no interface of their own.
This article explains Retrieval Augmented Generation (RAG), a method to reduce the risk of hallucinations in Large Language Models (LLMs) by limiting the context in which they generate answers. RAG is demonstrated using txtai, an open-source embeddings database for semantic search, LLM orchestration, and language model workflows.
This article provides an introduction to Mlflow, an open-source platform for end-to-end machine learning lifecycle management. The article focuses on using MLflow as an orchestrator for machine learning pipelines, explaining the importance of managing complex pipelines in machine learning projects.
Ragna is an open source RAG orchestration framework.
With an intuitive API for quick experimentation and built-in tools for creating production-ready application, you can quickly leverage Large Language Models (LLMs) for your work.
With an intuitive API for quick experimentation and built-in tools for creating production-ready application, you can quickly leverage Large Language Models (LLMs) for your work.
pip install 'ragna builtin » ' # Install ragna with all extensions
ragna config # Initialize configuration
ragna ui # Launch the web app
ragna config # Initialize configuration
ragna ui # Launch the web app
DSPy provides composable and declarative modules for instructing LMs in a familiar Pythonic syntax. It upgrades "prompting techniques" like chain-of-thought and self-reflection from hand-adapted string manipulation tricks into truly modular generalized operations that learn to adapt to your task.
