Llama-3-8B-Instruct-Gradient-1048k
Maintainer: gradientai
598
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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-Instruct-Gradient-1048k model is a large language model developed by Gradient that extends the context length of the original LLama-3 8B model from 8k to over 1048k tokens. It demonstrates that state-of-the-art LLMs can learn to operate on long context with minimal training by appropriately adjusting the Rotary Position Embedding (RoPE) theta. Gradient incorporated data from the SlimPajama dataset to train this model, which was then fine-tuned on 1.4B tokens over multiple stages with progressive increases in context length. This model builds on the Meta Llama-3-8B-Instruct base and shows improved performance on long-context tasks compared to the original LLama-3 8B model.
Model inputs and outputs
Inputs
- The model takes text-based inputs only.
Outputs
- The model generates text and code outputs.
Capabilities
The Llama-3-8B-Instruct-Gradient-1048k model is capable of engaging in open-ended dialogue, answering questions, summarizing text, and generating coherent text on a wide range of topics. Its increased context length allows it to maintain coherence and consistency over longer interactions compared to the original LLama-3 8B model.
What can I use it for?
This model can be used for a variety of natural language processing tasks, including chatbots, assistants, content generation, and code generation. The extended context length makes it particularly well-suited for applications that require maintaining coherence over long conversations or documents, such as task-oriented dialogues, long-form content creation, and knowledge-intensive applications.
Developers interested in building custom AI models or agents can contact Gradient to learn more about their end-to-end development service for large language models and AI systems.
Things to try
Try using the Llama-3-8B-Instruct-Gradient-1048k model for tasks that require maintaining context over long interactions, such as multi-turn dialogues, long-form document generation, or open-ended problem-solving. Experiment with different generation parameters and prompting strategies to see how the model's performance changes as the context length increases.
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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