With the advent of large language models (LLMs) such as GPT-3, Megatron-Turing, Chinchilla, PaLM-2, Falcon, and Llama 2, remarkable progress in natural language generation has been made in recent years. However, despite their ability to produce human-like text, foundation LLMs can fail to provide helpful and nuanced responses aligned with user preferences. The current approach to improving…
]]>With the increasing demand for access to pretrained large language model (LLM) weights, the climate around LLM sharing is changing. Recently, Meta released Open Pretrained Transformer, a language model with 175 billion parameters. BigScience is on schedule to release its multilingual language model with 176 billion parameters in a few months. As more LLMs become available…
]]>Big data, new algorithms, and fast computation are three main factors that make the modern AI revolution possible. However, data poses many challenges for enterprises: difficulty in data labeling, ineffective data governance, limited data availability, data privacy, and so on. Synthetically generated data is a potential solution to address these challenges because it generates data points by…
]]>Python is no stranger to data scientists. It ranks as the most popular computer language and is widely used for all kinds of tasks. Though Python is notoriously slow when the code is interpreted at runtime, many popular libraries make it run efficiently on GPUs for certain data science work. For example, popular deep learning frameworks such as TensorFlow, and PyTorch help AI researchers to…
]]>Imagine an AI program that can understand language better than humans can. Imagine building your own personal Siri or Google Search for a customized domain or application. Google BERT (Bidirectional Encoder Representations from Transformers) provides a game-changing twist to the field of natural language processing (NLP). BERT runs on supercomputers powered by NVIDIA GPUs to train its…
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