Nous-Capybara-34B – Local LLM with 200k context length

Nov 14, 2023 ·

This article focuses on the new Nous Research Capybara-34B language model, a large, powerful language model based on OpenAI's GPT-3 technology. The Capybara-34B is a transformer-based model that has been trained on up to 345 billion parameters and can generate output in over 100 different languages. The model is designed to produce contextualized and personalized text, allowing it to quickly learn to generate natural language text with little to no human intervention.

The model was built using the latest NLP technologies and has shown promising results in tasks such as summarization, question answering, sentiment analysis, and more. For example, in a recent paper, the authors showed that the model can accurately predict sentiment from data with an accuracy of 89%. Additionally, the model was able to achieve impressive results when used to generate text in a limited amount of time, showing that it can quickly adapt and produce accurate output even when given only limited training data.

The model also includes features to help ensure that generated text is both informative and coherent. To make sure that the generated text is objective, the model uses a feature called "style control," which helps the model adjust its writing style based on the type of text being generated. Additionally, the model uses a feature called "sentence completion," which allows the model to generate complete sentences without human input.

Overall, the Nous Research Capybara-34B language model is a powerful tool for natural language processing and can be used to generate a wide variety of text types with relatively little effort. With features like "style control" and "sentence completion," this model can quickly learn to generate natural language text and adapt to different styles depending on the task at hand. Additionally, the model has proven to be successful when used for summarization, sentiment analysis, and other tasks, making it a great choice for quickly generating high-quality text.