In this work, we highlight the following pitfall of prefilling: for batches containing high-varying prompt lengths, significant computation is wasted by the standard practice of padding sequences to the maximum length.
To this end, we propose a novel filter-based VINS framework named SchurVINS, which could guarantee both high accuracy by building a complete residual model and low computational complexity with Schur complement.
We outperform encoder-only models by a large margin on word-level tasks and reach a new unsupervised state-of-the-art performance on the Massive Text Embeddings Benchmark (MTEB).
After fine-tuning, Rho-1-1B and 7B achieved state-of-the-art results of 40. 6% and 51. 8% on MATH dataset, respectively - matching DeepSeekMath with only 3% of the pretraining tokens.
We introduce LLoCO, a technique that combines context compression, retrieval, and parameter-efficient finetuning using LoRA.
For data scaling, we introduce a Warmup-Stable-Decay (WSD) learning rate scheduler (LRS), conducive to continuous training and domain adaptation.
The Q-Formers are trained using paired images rather than the identical target, in which the reference image and the ground-truth image are with the same style or semantics.
To discriminate the difference in parameter scale, the research community has coined the term large language models (LLM) for the PLMs of significant size.
Our paper addresses the complex task of transferring a hairstyle from a reference image to an input photo for virtual hair try-on.
We introduce UFO, an innovative UI-Focused agent to fulfill user requests tailored to applications on Windows OS, harnessing the capabilities of GPT-Vision.