Large Language Models (LLMs) have recently demonstrated remarkable capabilities in
natural language processing tasks and beyond. This success of LLMs has led to a large …

Large Language Models (LLMs) went from non-existent to ubiquitous in the machine
learning discourse within a few years. Due to the fast pace of the field, it is difficult to identify …

The current trend of scaling language models involves increasing both parameter count and
training dataset size. Extrapolating this trend suggests that training dataset size may soon be …

We present DeepSeek-VL, an open-source Vision-Language (VL) Model designed for real-
world vision and language understanding applications. Our approach is structured around …

We introduce phi-1, a new large language model for code, with significantly smaller size
than competing models: phi-1 is a Transformer-based model with 1.3 B parameters, trained …

Large language models have an exceptional capability to incorporate new information in a
contextual manner. However, the full potential of such an approach is often restrained due to …

We continue the investigation into the power of smaller Transformer-based language
models as initiated by\textbf {TinyStories}--a 10 million parameter model that can produce …

We present Unified-IO 2 a multimodal and multi-skill unified model capable of following
novel instructions. Unified-IO 2 can use text images audio and/or videos as input and can …

Existing foundation models are trained on copyrighted material. Deploying these models
can pose both legal and ethical risks when data creators fail to receive appropriate …

We present Llemma, a large language model for mathematics. We continue pretraining
Code Llama on the Proof-Pile-2, a mixture of scientific papers, web data containing …