Awesome-LLM-Prompt-Optimization: a curated list of advanced prompt optimization and tuning methods in Large Language Models

Superpipe - optimized LLM pipelines for structured data

Implementation of the paper Fast Inference from Transformers via Speculative Decoding, Leviathan et al. 2023.

Nadir is a Python package designed to dynamically choose the best llm for your prompt by balancing complexity and cost and response time.

A Demo of Running Sleep-time Compute to Reduce LLM Latency

Adaptive Optimization Framework for AI Agents using Reinforcement Learning. Automatically optimize agent behaviors, reduce costs by 30%, and improve performance through Q-learning and data-driven prompt engineering.

Generate clean, AI-ready llms.txt files for your website or docs. Supports crawling, sitemaps, static builds, and framework-aware adapters (Next.js, Vite, Nuxt, Astro, Remix). Includes Markdown/MDX docs mode and robots.txt generator for LLM and search crawlers.

Research into neural network quantization with focus on domain-specific calibration and hardware-aware optimization. Committed to rigorous methodology and reproducible results.

🚄 Advanced LLM optimization techniques using CUDA. Features efficient attention mechanisms, custom CUDA kernels for transformers, and memory-efficient training strategies. ⚡

LLM-optimised prompt library for BusinessMate — the AI strategy coach for small business owners (SMB's) and solopreneurs.

DocuMerge for LLMs 🚀 Easily prepare GitHub repositories for LLM analysis!

Track your website’s LLM ranking performance with real-time insights.