  Text generation with Large Language Models (LLMs) is known to be memory bound
due to the combination of their auto-regressive nature, huge parameter counts,
and limited memory bandwidths, often resulting in low token rates. Speculative
decoding has been proposed as a solution for LLM inference acceleration.
However, since draft models are often unavailable in the modern open-source LLM
families, e.g., for Llama 2 7B, training a high-quality draft model is required
to enable inference acceleration via speculative decoding. In this paper, we
propose a simple draft model training framework for direct alignment to
chat-capable target models. With the proposed framework, we train Llama 2 Chat
Drafter 115M, a draft model for Llama 2 Chat 7B or larger, with only 1.64\% of
the original size. Our training framework only consists of pretraining,
distillation dataset generation, and finetuning with knowledge distillation,
with no additional alignment procedure. For the finetuning step, we use
instruction-response pairs generated by target model for distillation in
plausible data distribution, and propose a new Total Variation Distance++
(TVD++) loss that incorporates variance reduction techniques inspired from the
policy gradient method in reinforcement learning. Our empirical results show
that Llama 2 Chat Drafter 115M with speculative decoding achieves up to 2.3
block efficiency and 2.4$\times$ speed-up relative to autoregressive decoding
on various tasks with no further task-specific fine-tuning.
