初始化项目，由ModelHub XC社区提供模型
Model: rinna/gemma-2-baku-2b Source: Original Platform
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README.md
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README.md
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---
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thumbnail: https://github.com/rinnakk/japanese-pretrained-models/blob/master/rinna.png
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license: gemma
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datasets:
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- mc4
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- wikipedia
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- EleutherAI/pile
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- oscar-corpus/colossal-oscar-1.0
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- cc100
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language:
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- ja
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- en
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tags:
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- gemma2
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inference: false
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base_model: google/gemma-2-2b
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pipeline_tag: text-generation
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library_name: transformers
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---
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# `Gemma 2 Baku 2B (rinna/gemma-2-baku-2b)`
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# Overview
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We conduct continual pre-training of [google/gemma-2-2b](https://huggingface.co/google/gemma-2-2b) on **80B** tokens from a mixture of Japanese and English datasets. The continual pre-training improves the model's performance on Japanese tasks.
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The name `baku` comes from the Japanese word [`獏/ばく/Baku`](https://ja.wikipedia.org/wiki/獏), which is a kind of Japanese mythical creature ([`妖怪/ようかい/Youkai`](https://ja.wikipedia.org/wiki/%E5%A6%96%E6%80%AA)).
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| Size | Continual Pre-Training | Instruction-Tuning |
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| :- | :- | :- |
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| 2B | Gemma 2 Baku 2B [[HF]](https://huggingface.co/rinna/gemma-2-baku-2b) | Gemma 2 Baku 2B Instruct [[HF]](https://huggingface.co/rinna/gemma-2-baku-2b-it) |
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* **Library**
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The model was trained using code based on [Lightning-AI/litgpt](https://github.com/Lightning-AI/litgpt).
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* **Model architecture**
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A 26-layer, 2304-hidden-size transformer-based language model. Please refer to the [Gemma 2 Model Card](https://www.kaggle.com/models/google/gemma-2/) for detailed information on the model's architecture.
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* **Training**
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The model was initialized with the [google/gemma-2-2b](https://huggingface.co/google/gemma-2-2b) model and continually trained on around **80B** tokens from a mixture of the following corpora
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- [Japanese CC-100](https://huggingface.co/datasets/cc100)
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- [Japanese C4](https://huggingface.co/datasets/mc4)
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- [Japanese OSCAR](https://huggingface.co/datasets/oscar-corpus/colossal-oscar-1.0)
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- [The Pile](https://huggingface.co/datasets/EleutherAI/pile)
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- [Wikipedia](https://dumps.wikimedia.org/other/cirrussearch)
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- rinna curated Japanese dataset
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* **Contributors**
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- [Toshiaki Wakatsuki](https://huggingface.co/t-w)
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- [Xinqi Chen](https://huggingface.co/Keely0419)
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- [Kei Sawada](https://huggingface.co/keisawada)
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* **Release date**
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October 3, 2024
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---
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# Benchmarking
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Please refer to [rinna's LM benchmark page (Sheet 20241003)](https://rinnakk.github.io/research/benchmarks/lm/index.html).
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---
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# How to use the model
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~~~python
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import transformers
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import torch
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model_id = "rinna/gemma-2-baku-2b"
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pipeline = transformers.pipeline(
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"text-generation",
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model=model_id,
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model_kwargs={"torch_dtype": torch.bfloat16, "attn_implementation": "eager"},
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device_map="auto"
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)
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output = pipeline(
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"西田幾多郎は、",
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max_new_tokens=256,
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do_sample=True
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)
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print(output[0]["generated_text"])
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~~~
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It is recommended to use eager attention when conducting batch inference under bfloat16 precision.
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Currently, Gemma 2 yields NaN values for input sequences with padding when the default attention mechanism (torch.scaled_dot_product_attention) is employed in conjunction with bfloat16.
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---
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# Tokenization
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The model uses the original [google/gemma-2-2b](https://huggingface.co/google/gemma-2-2b) tokenizer.
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---
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# How to cite
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```bibtex
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@misc{rinna-gemma-2-baku-2b,
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title = {rinna/gemma-2-baku-2b},
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author = {Wakatsuki, Toshiaki and Chen, Xinqi and Sawada, Kei},
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url = {https://huggingface.co/rinna/gemma-2-baku-2b}
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}
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@inproceedings{sawada2024release,
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title = {Release of Pre-Trained Models for the {J}apanese Language},
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author = {Sawada, Kei and Zhao, Tianyu and Shing, Makoto and Mitsui, Kentaro and Kaga, Akio and Hono, Yukiya and Wakatsuki, Toshiaki and Mitsuda, Koh},
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booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)},
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month = {5},
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year = {2024},
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pages = {13898--13905},
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url = {https://aclanthology.org/2024.lrec-main.1213},
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note = {\url{https://arxiv.org/abs/2404.01657}}
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}
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```
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---
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# References
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```bibtex
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@article{gemma-2-2024,
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title = {Gemma 2},
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url = {https://www.kaggle.com/models/google/gemma-2},
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publisher = {Kaggle},
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author = {Gemma Team},
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year = {2024}
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}
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@misc{litgpt-2023,
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author = {Lightning AI},
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title = {LitGPT},
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howpublished = {\url{https://github.com/Lightning-AI/litgpt}},
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year = {2023}
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}
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```
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---
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# License
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[Gemma Terms of Use](https://ai.google.dev/gemma/terms)
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