Llama-3-Open-Ko-8B-Instruct-preview
Maintainer: beomi
51
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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-Open-Ko-8B-Instruct-preview model is a continuation of the Llama-3-8B model, developed by Junbum Lee (Beomi) and trained on over 60GB of publicly available Korean text data. This model uses the new Llama-3 tokenizer and was trained on 17.7B+ tokens, slightly more than the Llama-2-Ko tokenizer. The model was trained on TPUv5e-256 with support from the TRC program by Google. Applying ideas from the Chat Vector paper, the maintainer released an instruction-tuned version called the Llama-3-Open-Ko-8B-Instruct-preview, which serves as a great starting point for creating new chat and instruction models.
Model inputs and outputs
Inputs
- Text input only
Outputs
- Generates text and code
Capabilities
The Llama-3-Open-Ko-8B-Instruct-preview model is a capable text generation model that can handle a variety of tasks, from open-ended conversation to code generation. Its Korean language understanding and generation abilities make it well-suited for applications targeting Korean-speaking users.
What can I use it for?
The Llama-3-Open-Ko-8B-Instruct-preview model can be used for a wide range of natural language processing tasks, such as chatbots, language modeling, and text generation. Its instruction-tuned version is particularly well-suited for building conversational AI assistants that can engage in open-ended dialogue and help with various tasks. Developers can fine-tune the model further using their own data to create custom applications tailored to their specific needs.
Things to try
One interesting aspect of the Llama-3-Open-Ko-8B-Instruct-preview model is its potential for multilingual applications. While the model was primarily trained on Korean data, its underlying architecture is capable of generating text in multiple languages. Developers could explore fine-tuning the model on datasets in other languages to create versatile language models that can serve users across different linguistic backgrounds.
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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