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japanese-gpt-neox-3.6b

Maintainer: rinna

Total Score

88

Last updated 5/15/2024

🏋️

PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model Overview

The japanese-gpt-neox-3.6b is a 3.6 billion parameter Japanese language model developed by rinna. The model was trained using the EleutherAI/gpt-neox codebase on a dataset of over 312.5 billion Japanese tokens from sources like Japanese CC-100, Japanese C4, and Japanese Wikipedia. This results in a model with a validation perplexity of 8.68.

The model comes in several variants, including an instruction-following fine-tuned version (rinna/japanese-gpt-neox-3.6b-instruction-sft) and a reinforcement learning version (rinna/japanese-gpt-neox-3.6b-instruction-ppo). These variants allow the model to better understand and follow human instructions.

In comparison, the gpt-neox-20b model is a 20 billion parameter English language model trained by EleutherAI, while the mGPT model is a 1.3 billion parameter multilingual model developed by AI-Forever covering 61 languages. The gpt-j-6b model is a 6 billion parameter English language model developed by EleutherAI.

Model Inputs and Outputs

Inputs

  • Text prompts in Japanese for the model to continue and generate additional text.

Outputs

  • Continued Japanese text generated by the model based on the input prompt.

Capabilities

The japanese-gpt-neox-3.6b model can be used for a variety of Japanese language tasks, such as text generation, summarization, translation, and question answering. The model's strong performance on the Japanese language corpus allows it to generate coherent and contextually relevant Japanese text.

The fine-tuned variants of the model, like rinna/japanese-gpt-neox-3.6b-instruction-sft, demonstrate an even stronger ability to understand and follow human instructions, making them useful for building interactive Japanese language assistants or chatbots.

What Can I Use It For?

The japanese-gpt-neox-3.6b model can be a valuable tool for Japanese language researchers and developers. It can be used as a base model for fine-tuning on specific Japanese language tasks, or as a starting point for developing personalized Japanese language applications.

For example, a Japanese language tutoring app could use the model to generate natural Japanese responses to student prompts, providing an immersive language learning experience. Alternatively, a Japanese e-commerce platform could leverage the model's text generation capabilities to automatically produce product descriptions and summaries.

The instruction-following variants of the model, like rinna/japanese-gpt-neox-3.6b-instruction-sft, could be used to build sophisticated Japanese language assistants that can understand and execute complex user requests.

Things to Try

One interesting aspect of the japanese-gpt-neox-3.6b model is its ability to generate coherent and contextually relevant Japanese text. Try providing the model with a Japanese sentence or paragraph as a prompt and see how it continues the text. Observe how the model maintains the style, tone, and overall coherence of the generated output.

You can also experiment with the different variants of the model, like rinna/japanese-gpt-neox-3.6b-instruction-sft, and compare their performance on tasks that require understanding and following human instructions. This can give you insights into the model's robustness and potential 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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japanese-gpt-neox-3.6b-instruction-sft

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The japanese-gpt-neox-3.6b-instruction-sft is a 3.6 billion parameter Japanese GPT-NeoX model that has been fine-tuned to serve as an instruction-following conversational agent. The model was developed by Rinna, a maintainer on the Hugging Face platform. It is based on the pretrained rinna/japanese-gpt-neox-3.6b model and has been further fine-tuned on data from sources like the Anthropic HH RLHF dataset, the FLAN Instruction Tuning data, and the Stanford Human Preferences Dataset. This model can be compared to similar instruction-following language models like the Phi-3-mini-4k-instruct from Microsoft, which is a 3.8 billion parameter model fine-tuned on various datasets for instruction following and safety. Another related model is the mGPT from AI-Forever, which is a 1.3 billion parameter multilingual GPT model trained on 61 languages. Model inputs and outputs Inputs The model takes input prompts formatted as a conversation, with each utterance consisting of the speaker (" or "), a colon (:"), a space ( ), and the utterance text. The prompt should end with ": " to signal the model to generate a response. The model's tokenizer recognizes a special newline symbol "" instead of "\n". Outputs The model generates text continuations in response to the input prompt. Capabilities The japanese-gpt-neox-3.6b-instruction-sft model is capable of engaging in open-ended Japanese language conversations and following instructions. It can be used for tasks like question answering, summarization, and generation of responses tailored to the user's input. What can I use it for? This model could be useful for building Japanese language chatbots, virtual assistants, or other applications that require natural language processing and generation. The instruction-following capabilities make it well-suited for developing interactive applications where users can provide commands or requests to the system. Things to try One interesting aspect of this model is the use of a special input format with distinct speaker tags and a newline symbol. This format could enable more natural conversational interactions compared to plain text prompts. You could experiment with different types of prompts and conversation flows to see how the model responds. Additionally, since the model was fine-tuned on data related to instruction following and human preferences, it may be interesting to explore how the model handles more complex or nuanced requests or instructions. Trying out a variety of prompts, from simple commands to more open-ended tasks, could help uncover the model's strengths and limitations.

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