Hugging Face Researchers have unveiled Idefics2, an impressive 8-billion parameter vision-language model. It is designed to enhance the blending of text and image processing within a single framework. Unlike previous models which required the resizing of images to fixed dimensions, the Idefics2 model uses the Native Vision Transformers (NaViT) strategy to process images at their…
In our increasingly digital world, processing and understanding online content accurately and efficiently is becoming more crucial, especially for language processing systems. However, data extraction from web pages tends to produce cluttered and complicated data, posing a challenge to developers and users of language learning models looking for streamlined content for improved performance.
Previously, tools have…
In the highly competitive field of AI development, company Zyphra has announced a significant breakthrough with a new model called Zamba-7B. This compact model contains 7 billion parameters, but it competes favorably with larger models that are more resource-intensive. Key to the success of the Zamba-7B is a novel architectural design that improves both performance…
Language model-based machine learning systems, or LLMs, are reaching beyond their previous role in dialogue systems and are now actively participating in real-world applications. There is an increasing belief that many web interactions will be facilitated by systems driven by these LLMs. However, due to the complexities involved, humans are presently needed to verify the…
Pretrained language models (LMs) are essential tools in the realm of machine learning, often used for a variety of tasks and domains. But, adapting these models, also known as finetuning, can be expensive and time-consuming, especially for larger models. Traditionally, the solution to this issue has been to use Parameter-efficient finetuning (PEFT) methods such as…
The rapid improvement of large language models and their role in natural language processing has led to challenges in incorporating less commonly spoken languages. Embedding the majority of artificial intelligence (AI) systems in well-known languages inevitably forces a technological divide across linguistic communities that remains mostly unaddressed.
This paper introduces the SambaLingo system, a novel…
Large-scale language models (LLMs) have made substantial progress in understanding language by absorbing information from their environment. However, while they excel in areas like historical knowledge and insightful responses, they struggle when it comes to real-time comprehension. Embodied AI, integrated into items like smart glasses or home robots, aims to interact with humans using everyday…