5 papers · 1 filter
Baichuan 2: Open Large-scale Language Models
Aiyuan Yang, Bin Xiao, Bingning Wang +52
Large language models (LLMs) have demonstrated remarkable performance on a variety of natural language tasks based on just a few examples of natural language instructions, reducing…
Boosting Lossless Speculative Decoding via Feature Sampling and Partial Alignment Distillation
Lujun Gui, Bin Xiao, Lei Su +1
Lossless speculative decoding accelerates target large language model (LLM) inference by employing a lightweight draft model for generating tree-structured candidates, which are su…
BaichuanSEED: Sharing the Potential of ExtensivE Data Collection and Deduplication by Introducing a Competitive Large Language Model Baseline
Guosheng Dong, Da Pan, Yiding Sun +17
The general capabilities of Large Language Models (LLM) highly rely on the composition and selection on extensive pretraining datasets, treated as commercial secrets by several ins…
Clover-2: Accurate Inference for Regressive Lightweight Speculative Decoding
Bin Xiao, Lujun Gui, Lei Su +1
Large Language Models (LLMs) frequently suffer from inefficiencies, largely attributable to the discord between the requirements of auto-regressive decoding and the architecture of…
Clover: Regressive Lightweight Speculative Decoding with Sequential Knowledge
Bin Xiao, Chunan Shi, Xiaonan Nie +5
Large language models (LLMs) suffer from low efficiency as the mismatch between the requirement of auto-regressive decoding and the design of most contemporary GPUs. Specifically,…