LLaMA-2-7B-32K
Maintainer: togethercomputer
522
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| Property | Value |
|---|---|
| Run this model | Run 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
LLaMA-2-7B-32K is an open-source, long context language model developed by Together, fine-tuned from Meta's original Llama-2 7B model. This model extends the context length to 32K with position interpolation, allowing applications on multi-document QA, long text summarization, and more. Compared to similar models like Llama-2-13b-chat-hf, Llama-2-7b-hf, Llama-2-13b-hf, and Llama-2-70b-chat-hf, this model focuses on handling longer contexts.
Model inputs and outputs
Inputs
- Text input
Outputs
- Generated text
Capabilities
LLaMA-2-7B-32K can handle context lengths up to 32K, making it suitable for applications that require processing of long-form content, such as multi-document question answering and long text summarization. The model has been fine-tuned on a mixture of pre-training and instruction tuning data to improve its few-shot capabilities under long context.
What can I use it for?
You can use LLaMA-2-7B-32K for a variety of natural language generation tasks that benefit from long-form context, such as:
- Multi-document question answering
- Long-form text summarization
- Generating coherent and informative responses to open-ended prompts that require drawing upon a large context
The model's extended context length and fine-tuning on long-form data make it well-suited for these kinds of applications.
Things to try
One interesting aspect of LLaMA-2-7B-32K is its ability to leverage long-range context to generate more coherent and informative responses. You could try providing the model with multi-paragraph prompts or documents and see how it performs on tasks like summarization or open-ended question answering, where the additional context can help it generate more relevant and substantive outputs.
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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