Which LLM framework(s) do you use in production and why?

Dec 6, 2023 ·

Many organizations have started using language model frameworks in production due to their immense potential for natural language processing. These frameworks allow machines to learn from data and generate human-like outputs.

One of the most widely used frameworks is GPT-2, developed by OpenAI, which stands for Generative Pre-trained Transformer. It is an unsupervised language model that can be fine-tuned to solve specific tasks. GPT-2 uses a deep learning technique called transformer architecture to predict words and sentences based on context. It is capable of generating high-quality natural language text with minimal training data.

BERT (Bidirectional Encoder Representations from Transformers) is another popular language model framework developed by Google. It is designed to help machines learn from large data sets quickly and accurately. BERT uses a bidirectional architecture, meaning it takes into account both past and future words when predicting the next word in a sentence. BERT also makes use of a technique called “masking” to prevent overfitting.

XLM (Cross-Lingual Language Model) is a language model developed by Facebook AI Research. It is designed to enable machines to understand many languages at once, allowing for cross-lingual transfer learning. This model uses a transformer architecture to learn from data in multiple languages simultaneously. It is particularly useful for building multilingual applications that need to understand multiple languages.

These are just a few of the language model frameworks used today in production. Each framework offers unique benefits and capabilities that make them ideal for different types of natural language processing tasks. By understanding the strengths and weaknesses of each framework, organizations can choose the best option for their needs.