HuggingFace Introduces TextEnvironments: An Orchestrator between a Machine Learning Model and A Set of Tools (Python Functions) that the Model can Call to Solve Specific Tasks

Supervised Fine-tuning (SFT), Reward Modeling (RM), and Proximal Policy Optimization (PPO) are all part of TRL. In this full-stack library, researchers give tools to train transformer language models and stable diffusion models with Reinforcement Learning. The library is an extension of Hugging Face's transformers collection. Therefore, language models can be loaded directly via transformers after they have been pre-trained. Most decoder and encoder-decoder designs are currently supported. For code snippets and instructions on how to use these programs, please consult the manual or the examples/ subdirectory. Highlights Easily tune language models or adapters on a custom dataset with the help

This is a companion discussion topic for the original entry at https://www.marktechpost.com/2023/11/17/huggingface-introduces-textenvironments-an-orchestrator-between-a-machine-learning-model-and-a-set-of-tools-python-functions-that-the-model-can-call-to-solve-specific-tasks/