speculative-decoding

What problem does it solve?

This Skill solves the problem of slow LLM inference and high latency, which are critical bottlenecks for real-time AI applications and efficient model deployment on limited hardware. It enables you to achieve significant speedups without compromising model quality.

Core Features & Use Cases

• Accelerated Inference: Achieve 1.5-3.6× speedup in LLM inference without any loss in output quality.
• Reduced Latency: Drastically cut down response times, making real-time applications like chatbots and code generation more responsive.
• Efficient Deployment: Optimize throughput and deploy large language models more efficiently on hardware with limited computational resources.
• Key Techniques: Leverages advanced methods including draft model speculative decoding, Medusa (multiple decoding heads), and Lookahead Decoding (Jacobi iteration).
• Use Case: Deploy a high-volume customer service chatbot that needs to respond instantly, or a code generation tool that provides real-time suggestions, all while minimizing compute costs.

Quick Start

Use speculative decoding to generate a 256-token response to "Explain quantum computing in simple terms:" using a Llama-2-70b-hf target model and a Llama-2-7b-hf draft model.