Yarn-Llama-2-13b-128k
Maintainer: NousResearch
113
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| Property | Value |
|---|---|
| Model Link | View on HuggingFace |
| API Spec | View on HuggingFace |
| Github Link | No Github link provided |
| Paper Link | No paper link provided |
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Model overview
The Yarn-Llama-2-13b-128k model is a state-of-the-art language model developed by NousResearch. It is a further pretrained version of the original Yarn-Llama-2-13b-128k model, with additional training on long context data for 600 steps. This model is capable of effectively utilizing up to 128k tokens of context.
Model inputs and outputs
The Yarn-Llama-2-13b-128k model is a text-to-text transformer model, meaning it takes text as input and generates text as output. It does not have any specific prompt format requirements, as it is a pretrained base model.
Inputs
- Text inputs of variable length
Outputs
- Text outputs of variable length
Capabilities
The Yarn-Llama-2-13b-128k model is designed for long-context natural language tasks. It has been further pretrained on long context data, allowing it to effectively utilize up to 128k tokens of context. This makes it well-suited for tasks that require understanding and generating long-form text, such as summarization, question-answering, and creative writing.
What can I use it for?
The Yarn-Llama-2-13b-128k model can be used for a wide range of natural language processing tasks, including:
- Text generation: The model can be used to generate coherent and contextually-relevant text, such as articles, stories, or dialogues.
- Question answering: The model can be used to answer questions based on provided context, leveraging its long-form understanding capabilities.
- Summarization: The model can be used to generate concise summaries of long-form text.
- Dialogue systems: The model can be used as a conversational agent, responding to user inputs in a natural and contextually-appropriate manner.
Things to try
One interesting aspect of the Yarn-Llama-2-13b-128k model is its ability to effectively utilize long-form context. This can be particularly useful for tasks that require understanding and reasoning about complex, multi-paragraph information. Try experimenting with providing the model with detailed background information or lengthy prompts and see how it is able to generate coherent and relevant responses.
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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