Llama-3.1-SuperNova-Lite
Maintainer: arcee-ai
133
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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-3.1-SuperNova-Lite is an 8B parameter model developed by Arcee.ai, based on the Llama-3.1-8B-Instruct architecture. It is a distilled version of the larger Llama-3.1-405B-Instruct model, leveraging offline logits extracted from the 405B parameter variant. This 8B variation of Llama-3.1-SuperNova maintains high performance while offering exceptional instruction-following capabilities and domain-specific adaptability.
The model was trained using a state-of-the-art distillation pipeline and an instruction dataset generated with EvolKit, ensuring accuracy and efficiency across a wide range of tasks. Llama-3.1-SuperNova-Lite excels in both benchmark performance and real-world applications, providing the power of large-scale models in a more compact, efficient form ideal for organizations seeking high performance with reduced resource requirements.
Model inputs and outputs
Inputs
- Text
Outputs
- Text
Capabilities
Llama-3.1-SuperNova-Lite excels at a variety of text-to-text tasks, including instruction-following, open-ended question answering, and knowledge-intensive applications. The model's distilled architecture maintains the strong performance of its larger counterparts while being more resource-efficient.
What can I use it for?
The compact and powerful nature of Llama-3.1-SuperNova-Lite makes it an excellent choice for organizations looking to leverage the capabilities of large language models without the resource requirements. Potential use cases include chatbots, content generation, question-answering systems, and domain-specific applications that require high-performing text-to-text capabilities.
Things to try
Explore how Llama-3.1-SuperNova-Lite performs on your specific text-to-text tasks, such as generating coherent and informative responses to open-ended prompts, following complex instructions, or answering knowledge-intensive questions. The model's strong instruction-following abilities and domain-specific adaptability make it a versatile tool for a wide range of applications.
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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