Zi Yang

COMQ: A Backpropagation-Free Algorithm for Post-Training Quantization

2025 · IEEE Access

Post-training quantization (PTQ) has emerged as a practical approach to compress large neural networks, making them highly efficient for deployment. However, effectively reducing these models to their low-bit counterparts without compromising the original accuracy remains …

DiaBlo: Diagonal Blocks Are Sufficient For Finetuning

2025 · arXiv (Cornell University)

Fine-tuning is a critical step for adapting large language models (LLMs) to domain-specific downstream tasks. To mitigate the substantial computational and memory costs of full-model fine-tuning, Parameter-Efficient Fine-Tuning (PEFT) methods have been proposed to update …

Towards a Human-like Open-Domain Chatbot

2020 · arXiv (Cornell University)

We present Meena, a multi-turn open-domain chatbot trained end-to-end on data mined and filtered from public domain social media conversations. This 2.6B parameter neural network is simply trained to minimize perplexity of the next token. …