We show successful replication and fine-tuning of foundational models like CLIP, GLIDE and Stable Diffusion using the dataset, and discuss further experiments enabled with an openly available dataset of this scale.
As such, we study the challenging problem of task oriented detection, which aims to find objects that best afford an action indicated by verbs like sit comfortably on.
In this work, we present a conceptually simple and effective method to train a strong bilingual/multilingual multimodal representation model.
First, we use synthetic language modeling tasks to understand the gap between SSMs and attention.
We call the collected dataset the Human ChatGPT Comparison Corpus (HC3).
We propose SmoothQuant, a training-free, accuracy-preserving, and general-purpose post-training quantization (PTQ) solution to enable 8-bit weight, 8-bit activation (W8A8) quantization for LLMs that can be implemented efficiently.
We call our method generate-then-read (GenRead), which first prompts a large language model to generate contextutal documents based on a given question, and then reads the generated documents to produce the final answer.
To further exploit the potential of the transformer, we propose a novel flexible window training strategy.