2 Repos
Training methodologies that incorporate live code execution to verify model outputs during learning.
Distinguishing note: Focuses on the integration of interpreters into the training loop for accuracy improvement.
Explore 2 awesome GitHub repositories matching artificial intelligence & ml · Code-Integrated Training Frameworks. Refine with filters or upvote what's useful.
Open-r1 is a framework designed for the large-scale training, distillation, and optimization of language models focused on complex reasoning and programming tasks. It provides a comprehensive suite of tools for managing distributed training jobs across multi-node clusters, enabling the development of high-performance models through reinforcement learning and supervised fine-tuning. The project distinguishes itself by integrating secure, containerized code execution environments directly into the training and evaluation lifecycle. By allowing models to run and verify code snippets against test
Integrates code execution environments during training to verify outputs and improve problem-solving accuracy.
Aim is an open-source platform for logging, visualizing, and comparing machine learning training runs and LLM traces. It provides a remote tracking server and a comparison UI, functioning as an ML experiment tracker, AI workflow logger, and LLM trace recorder that captures prompts, generations, and tool calls from AI applications. The platform distinguishes itself through a run-based data model with local SQLite storage, real-time metric streaming, and a plugin-based explorer system that supports specialized visual analysis of metrics, images, audio, and text. It offers a Python SDK with cont
Connects to popular ML frameworks like PyTorch and TensorFlow for logging training runs with minimal code changes.