This repository provides a collection of research initiatives and technologies focused on enhancing the capabilities of Large Language Models (LLMs) and Generative AI. It targets AI researchers and developers building foundation model-based products, offering solutions for prompt optimization, longer context handling, LLM alignment, inference acceleration, and domain customization.

How It Works

The project explores several novel approaches to LLM interaction and performance. "Prompt Intelligence" utilizes reinforcement learning (Promptist) and structured prompting to optimize user inputs into model-preferred formats, enabling efficient handling of long contexts and scaling in-context learning. "LLM Accelerators" (LLMA) achieve significant inference speed-ups (2-3x) without additional models by leveraging reference text, applicable to scenarios like retrieval-augmented generation. Fundamental research investigates in-context learning (ICL) as a form of meta-optimization within Transformers, akin to implicit finetuning.

Quick Start & Requirements

• Installation and usage details are not explicitly provided in the README. Specific components may require individual setup based on their respective research papers and code releases.
• Prerequisites will vary per component but likely include Python, deep learning frameworks (PyTorch), and potentially specific hardware (GPUs) for running LLMs.
• Links to relevant papers, models, and demos are provided for individual components.

Highlighted Details

• Automatic prompt optimization using "gradient descent" and beam search.
• Structured prompting to scale in-context learning to 1,000 examples.
• Lossless LLM acceleration achieving 2-3x speed-up via reference copying.
• Research into LLMs performing implicit finetuning as meta-optimizers for in-context learning.

Maintenance & Community

This is a research initiative by Microsoft, with contributions from researchers like Furu Wei. Several papers have been released recently (Oct-Nov 2023), indicating active development. Contact information for inquiries and hiring is provided.

Licensing & Compatibility

The project is licensed under the terms found in the LICENSE file. Specific compatibility for commercial use or closed-source linking would require reviewing the LICENSE file.

Limitations & Caveats

The README outlines a research agenda rather than a production-ready library. Specific components are presented as research papers and code releases, implying varying levels of maturity, documentation, and stability. A comprehensive quick-start guide or unified installation process is not present.