Search Results for author:
Found 13 papers, 8 papers with code
VILA-U: a Unified Foundation Model Integrating Visual Understanding and Generation
VILA-U is a Unified foundation model that integrates Video, Image, Language understanding and generation.
LongVILA: Scaling Long-Context Visual Language Models for Long Videos
We introduce the long-context Multi-Modal Sequence Parallelism (MM-SP) system that efficiently parallelizes long video training and inference, enabling 2M context length training on 256 GPUs without any gradient checkpointing.
MPC-Minimized Secure LLM Inference
Many inference services based on large language models (LLMs) pose a privacy concern, either revealing user prompts to the service or the proprietary weights to the user.
SORRY-Bench: Systematically Evaluating Large Language Model Safety Refusal Behaviors
First, existing methods often use coarse-grained taxonomies of unsafe topics, and are over-representing some fine-grained topics.
Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference
To address this issue, we introduce Chatbot Arena, an open platform for evaluating LLMs based on human preferences.
Fairness in Serving Large Language Models
High-demand LLM inference services (e. g., ChatGPT and BARD) support a wide range of requests from short chat conversations to long document reading.
S-LoRA: Serving Thousands of Concurrent LoRA Adapters
To capitalize on these opportunities, we present S-LoRA, a system designed for the scalable serving of many LoRA adapters.
DISTFLASHATTN: Distributed Memory-efficient Attention for Long-context LLMs Training
FlashAttention (Dao, 2023) effectively reduces the quadratic peak memory usage to linear in training transformer-based large language models (LLMs) on a single GPU.
Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena
Evaluating large language model (LLM) based chat assistants is challenging due to their broad capabilities and the inadequacy of existing benchmarks in measuring human preferences.
Ranked #3 on
Long-Context Understanding
on Ada-LEval (TSort)
Does compressing activations help model parallel training?
Finally, we provide insights for future development of model parallelism compression algorithms.
MPCFormer: fast, performant and private Transformer inference with MPC
Through extensive evaluations, we show that MPCFORMER significantly speeds up Transformer inference in MPC settings while achieving similar ML performance to the input model.
AMP: Automatically Finding Model Parallel Strategies with Heterogeneity Awareness
Scaling up model sizes can lead to fundamentally new capabilities in many machine learning (ML) tasks.
Dual Contradistinctive Generative Autoencoder
Our model, named dual contradistinctive generative autoencoder (DC-VAE), integrates an instance-level discriminative loss (maintaining the instance-level fidelity for the reconstruction/synthesis) with a set-level adversarial loss (encouraging the set-level fidelity for there construction/synthesis), both being contradistinctive.
Ranked #2 on
Image Generation
on LSUN Bedroom 128 x 128
