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Overview of Japanese LLMs

A community-maintained, regularly updated overview of publicly available Japanese large language models and their benchmarks, listing each model's architecture, training data, publisher and license.

Overview of Japanese LLMs

This repository is a community-maintained overview of Japanese large language models, models trained mainly on Japanese text, and of benchmarks for evaluating them. Entries are contributed by volunteers and partly drawn from papers and public resources, and the maintainers note that the list does not guarantee completeness or accuracy and may not always reflect the newest models.

For text-only and vision-and-language models, tables list details such as release year, architecture, context length, training data, the organization behind the model, and its license or terms of use; many models use permissive licenses such as MIT or Apache-2.0, but some carry non-commercial or vendor-specific licenses that are not fully open source. A separate, more readable web version of the same content is linked from the README.

More in Models

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  • mini-AGI

    github.com

    A continual learning language model that trains from scratch on a single 8 GB VRAM GPU, assembling its own architecture and adapting its size to the hardware and data available rather than shipping as a fixed pretrained model.

  • An open-source contrastive-embedding pipeline that maps technical CVE vulnerability descriptions to formal cybersecurity compliance controls in NIST SP 800-53 and CMMC, published with its model generation, fine-tuning and evaluation code.

  • peekaboolean

    github.com

    A small vision-language model, fine-tuned with LoRA on a 500-million-parameter backbone, that answers typed multiple-choice, ordinal-score and yes/no questions about an image with calibrated probabilities, in about 400 milliseconds on a laptop.