Llama-3-8B-16K
Maintainer: mattshumer
113
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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
The Llama-3-8B-16K is an extended version of the LLaMA 3 8B model, which was developed and released by Meta. This model has a context length of 16,384 tokens, compared to the base LLaMA 3 8B model's 8,192 tokens. It was trained for 5 hours on 8 A6000 GPUs using the Yukang/LongAlpaca-16k-length dataset. The maintainer, mattshumer, set the rope_theta parameter to 1,000,000.0 and used the Axolotl training library.
Similar models to the Llama-3-8B-16K include the Llama-3-8b-64k-PoSE and the Llama-3-8B-Instruct-262k models, which also extend the context length of the LLaMA 3 8B model.
Model inputs and outputs
Inputs
- Text: The
Llama-3-8B-16Kmodel takes in text as input.
Outputs
- Text: The model generates text as output.
Capabilities
The Llama-3-8B-16K model is a text-to-text model, capable of generating text based on the provided input. The extended context length of 16,384 tokens allows the model to work with longer input sequences compared to the base LLaMA 3 8B model.
What can I use it for?
The Llama-3-8B-16K model can be used for a variety of natural language generation tasks, such as text summarization, language translation, and content creation. The extended context length may be particularly useful for applications that require processing longer input texts, such as long-form articles or research papers.
Things to try
One interesting aspect of the Llama-3-8B-16K model is the use of the rope_theta parameter, which was set to a high value of 1,000,000.0. This parameter is related to the Rotary Position Embedding (RoPE) technique, which can help the model better understand the positional relationships within the input text. Experimenting with different rope_theta values may lead to further performance improvements, particularly for tasks that require a strong understanding of long-range dependencies.
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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