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 introduction of ChatGPT has led to a significant increase in the utilization of Large
Language Models (LLMs) for addressing downstream tasks. There's an increasing focus on …

Among the widely used parameter-efficient fine-tuning (PEFT) methods, LoRA and its
variants have gained considerable popularity because of avoiding additional inference …

More than three billion years of evolution have produced an image of biology encoded into
the space of natural proteins. Here we show that language models trained at scale on …

We introduce Cambrian-1, a family of multimodal LLMs (MLLMs) designed with a vision-
centric approach. While stronger language models can enhance multimodal capabilities, the …

The popularity of LLaMA (Touvron et al., 2023a; b) and other recently emerged moderate-
sized large language models (LLMs) highlights the potential of building smaller yet powerful …

Rotary Position Embeddings (RoPE) have been shown to effectively encode positional
information in transformer-based language models. However, these models fail to …

Diffusion models have achieved great success in synthesizing high-quality images.
However generating high-resolution images with diffusion models is still challenging due to …

Language models (LMs) have become ubiquitous in both NLP research and in commercial
product offerings. As their commercial importance has surged, the most powerful models …

We introduce LlamaGen, a new family of image generation models that apply original``next-
token prediction''paradigm of large language models to visual generation domain. It is an …