6 papers
Understanding Calibration and Truncation Error Propagation in Training-Free Low-Rank Compression for LLMs
Mohanad Odema, Gabrielle De Micheli, Dayin Gou +3
Training-free low-rank compression frameworks have been gaining prominence for LLM compression given their effectiveness in reducing model parameter count while maintaining task-le…
3-Model Speculative Decoding
Sanghyun Byun, Mohanad Odema, Jung Ick Guack +3
Speculative Decoding (SD) accelerates inference in large language models by using a smaller draft model to propose tokens, which are then verified by a larger target model. However…
Unifying Vision-Language Latents for Zero-label Image Caption Enhancement
Sanghyun Byun, Jung Ick Guack, Mohanad Odema +3
Vision-language models (VLMs) achieve remarkable performance through large-scale image-text pretraining. However, their reliance on labeled image datasets limits scalability and le…
CARVQ: Corrective Adaptor with Group Residual Vector Quantization for LLM Embedding Compression
Dayin Gou, Sanghyun Byun, Nilesh Malpeddi +4
Large Language Models (LLMs) typically rely on a large number of parameters for token embedding, leading to substantial storage requirements and memory footprints. In particular, L…
APCE: Adaptive Progressive Context Expansion for Long Context Processing
Baisub Lee, Sanghyun Byun, Mohanad Odema +3
Deploying useful Long-Context Transformer Models (LCTMs) requires addressing two key challenges: (1) A growing memory footprint due to quadratic self-attention and linear KV-cache…
HunyuanVideo: A Systematic Framework For Large Video Generative Models
Weijie Kong, Qi Tian, Zijian Zhang +49
Recent advancements in video generation have significantly impacted daily life for both individuals and industries. However, the leading video generation models remain closed-sourc…