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Nous-Capybara-34B

Maintainer: NousResearch

Total Score

225

Last updated 5/16/2024

🏷️

PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

The Nous-Capybara-34B V1.9 is the first 34B Nous model and the first 200K context length Nous model, trained by Nous Research. It was fine-tuned on the Capybara dataset, which leverages Nous' novel "Amplify-Instruct" data synthesis technique. This technique combines top-performing data synthesis methods like Airoboros, Evol-Instruct (WizardLM), Orca, Vicuna, Know_Logic, Lamini, and FLASK, along with seed instructions from datasets like Airoboros, Know Logic, EverythingLM, GPTeacher, and LessWrong. The current Capybara dataset contains 20K training examples, which is 10 times smaller than many similar performing models. This has significant scaling implications for Nous' future generations of models.

The model was fine-tuned by Nous Research as part of the Capybara/Amplify-Instruct project led by Luigi D. (LDJ), with significant dataset formation contributions from J-Supha and general compute and experimentation management by Jeffrey Q. The training was sponsored by A16Z and Yield Protocol.

Model inputs and outputs

The Nous-Capybara-34B is a text-to-text AI model that can take in a wide range of textual inputs and generate relevant responses. The model is trained on a large corpus of diverse data, enabling it to handle a variety of tasks and queries.

Inputs

  • Freeform text prompts or queries
  • Conversational exchanges
  • Instructions or requests for information, analysis, or task completion

Outputs

  • Relevant and coherent textual responses
  • Informative and well-reasoned answers to questions
  • Detailed plans or step-by-step instructions for completing tasks
  • Creative and engaging text generation

Capabilities

The Nous-Capybara-34B model is capable of tackling a wide range of language tasks, from natural language understanding and generation to following complex instructions and completing multi-step tasks. It can engage in substantive conversations, provide detailed explanations and analyses, and generate creative and coherent text.

One key capability of the model is its long-form response generation, which allows it to produce detailed and nuanced outputs. It also exhibits a low hallucination rate, meaning it is less prone to generating factually incorrect information. Additionally, the model is not subject to the censorship mechanisms found in some other large language models.

What can I use it for?

The Nous-Capybara-34B model is a versatile tool that can be applied to a variety of projects and use cases. Some potential applications include:

  • Building advanced chatbots and virtual assistants to handle complex queries and tasks
  • Automating content generation for blogs, articles, or other written materials
  • Enhancing language understanding and generation capabilities in various software applications
  • Powering research and analysis tools that require in-depth textual processing and generation

For example, you could use the Nous-Capybara-34B model to build a virtual assistant that can engage in detailed conversations, provide step-by-step instructions for completing tasks, and generate creative and informative text. This could be useful for customer service, educational, or research applications.

Things to try

One interesting aspect of the Nous-Capybara-34B model is its ability to generate long, coherent responses. You could experiment with prompting the model to elaborate on a specific topic or provide a detailed analysis of a complex issue. This could help you uncover the model's depth of knowledge and its capacity for nuanced and thoughtful discourse.

Another area to explore is the model's performance on multi-step tasks or instructions. You could provide the model with a set of requirements or a problem to solve, and see how it breaks down the problem and outlines a comprehensive solution. This could be particularly useful for applications that require task planning and execution.

Overall, the Nous-Capybara-34B model represents an exciting advancement in large language model technology, with the potential to enable a wide range of innovative applications and use cases.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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