Open-source alternatives to RAP

We instantiate Pangu on knowledge base question answering (KBQA), which is representative testbed for grounded language understanding with a highly complex and heterogeneous environment.

Transformers is a comprehensive library for machine learning that provides a unified interface for training, fine-tuning, and deploying transformer-based models. It supports a wide range of tasks, including text classification, language modeling, question answering, and sequence-to-sequence translation, while offering specialized architectures for both text and vision processing. The framework includes tools for managing the entire model lifecycle, from data preprocessing and tokenization to distributed training and inference. The library features extensive support for model optimization and

With Self-Refine, LLMs can generate feedback on their work, use it to improve the output, and repeat this process.

JARVIS is a system for large language model task orchestration, deployment management, and automation benchmarking. It utilizes a task orchestrator to decompose complex requests into actionable steps and coordinates various expert models to synthesize final responses. The project includes an AI model deployment manager to handle the local deployment of expert models across different hardware scales. It further provides an AI workflow API consisting of web endpoints used to trigger automated task workflows and retrieve results from model selection stages. The framework incorporates an automat

Note: https://github.com/kyegomez/tree-of-thoughts CANNOT replicate paper results.