Llama-3-ELYZA-JP-8B
Maintainer: elyza
60
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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-ELYZA-JP-8B is a large language model developed by ELYZA, Inc that has been enhanced for Japanese usage. It is based on the meta-llama/Meta-Llama-3-8B-Instruct model, but has undergone additional pre-training and instruction tuning to improve its performance on Japanese tasks. The model was developed by a team of researchers and engineers, including Masato Hirakawa, Shintaro Horie, Tomoaki Nakamura, Daisuke Oba, Sam Passaglia, and Akira Sasaki.
Model inputs and outputs
Inputs
- Text: The model takes in text input, which can be used for tasks such as language generation, translation, and summarization.
Outputs
- Text: The model generates text output, which can be used for a variety of natural language processing tasks.
Capabilities
The Llama-3-ELYZA-JP-8B model has been trained to perform well on a variety of Japanese language tasks, including dialogue, question answering, and code generation. The model's enhanced performance on Japanese tasks makes it a useful tool for developers and researchers working with Japanese language data.
What can I use it for?
The Llama-3-ELYZA-JP-8B model can be used for a variety of natural language processing tasks in the Japanese language, such as:
- Language generation: The model can be used to generate human-like text in Japanese, which can be useful for applications like chatbots, content creation, and language learning.
- Translation: The model can be used to translate text between Japanese and other languages, which can be useful for international communication and collaboration.
- Question answering: The model can be used to answer questions in Japanese, which can be useful for building knowledge-based applications and virtual assistants.
Things to try
One interesting thing to try with the Llama-3-ELYZA-JP-8B model is to use it for code generation in Japanese. The model's ability to understand and generate Japanese text can make it a useful tool for developers working on Japanese-language software projects. You can also try fine-tuning the model on specific Japanese language tasks or datasets to further improve its performance.
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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