ELYZA-japanese-Llama-2-7b

Maintainer: elyza

Total Score

79

Last updated 5/27/2024

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PropertyValue
Run this modelRun on HuggingFace
API specView on HuggingFace
Github linkNo Github link provided
Paper linkNo paper link provided

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Model overview

The ELYZA-japanese-Llama-2-7b is a large language model based on the Llama 2 architecture developed by Meta. It has been fine-tuned by elyza to work with Japanese language inputs and outputs. Similar models in the ELYZA-japanese-Llama-2-7b series include the ELYZA-japanese-Llama-2-7b-instruct, ELYZA-japanese-Llama-2-7b-fast, and ELYZA-japanese-Llama-2-7b-fast-instruct models, which offer different capabilities and performance characteristics.

Model inputs and outputs

Inputs

  • The ELYZA-japanese-Llama-2-7b model accepts Japanese language text as input.

Outputs

  • The model generates Japanese language text in response to the input.

Capabilities

The ELYZA-japanese-Llama-2-7b model is capable of a variety of natural language processing tasks, such as text generation, language translation, and question answering. Its fine-tuning on Japanese data allows it to perform well on tasks requiring understanding and generation of Japanese text.

What can I use it for?

The ELYZA-japanese-Llama-2-7b model could be useful for a range of applications, including:

  • Developing Japanese language chatbots or virtual assistants
  • Translating between Japanese and other languages
  • Generating Japanese text for content creation or summarization
  • Answering questions or providing information in the Japanese language

Things to try

One interesting aspect of the ELYZA-japanese-Llama-2-7b model is its potential for generating coherent and contextually appropriate Japanese text. Developers could experiment with prompting the model to write short stories, poems, or even news articles in Japanese to see the quality and creativity of the output.



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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