Llama-3-Smaug-8B
Maintainer: abacusai
80
📊
| 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-Smaug-8B is a large language model developed by Abacus.AI using the Smaug recipe for improving performance on real world multi-turn conversations. It is built on top of the meta-llama/Meta-Llama-3-8B-Instruct model. Compared to the base Meta-Llama-3-8B-Instruct model, this version uses new techniques and new data that allow it to outperform on key benchmarks like MT-Bench.
Model inputs and outputs
The Llama-3-Smaug-8B model takes in text as input and generates text as output. It is designed for open-ended natural language tasks and can be used for a variety of applications, from language generation to question answering.
Inputs
- Text prompts for the model to continue or respond to
Outputs
- Continuation of the input text
- Answers to questions
- Descriptions, summaries, or other text generation tasks
Capabilities
The Llama-3-Smaug-8B model is capable of engaging in multi-turn conversations and performing well on a variety of language understanding and generation benchmarks. It outperforms the base Meta-Llama-3-8B-Instruct model on the MT-Bench evaluation, achieving higher scores on both the first and second turns.
What can I use it for?
The Llama-3-Smaug-8B model can be used for a wide range of natural language processing tasks, including:
- Building conversational AI assistants
- Generating human-like text for creative writing or content creation
- Answering questions and providing information
- Summarizing long-form text
- Translating between languages
The model's strong performance on multi-turn conversations makes it well-suited for developing interactive chatbots and virtual assistants.
Things to try
One interesting thing to try with the Llama-3-Smaug-8B model is generating multi-turn dialogues. The model's ability to maintain context and coherence across turns allows for the creation of more natural and engaging conversations. You could also experiment with using the model for creative writing, task-oriented dialogue, or other applications that require sustained language generation.
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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