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---
thumbnail: https://github.com/rinnakk/japanese-pretrained-models/blob/master/rinna.png
license: gemma
datasets:
- mc4
- wikipedia
- EleutherAI/pile
- oscar-corpus/colossal-oscar-1.0
- cc100
language:
- ja
- en
tags:
- gemma2
inference: false
base_model: google/gemma-2-2b
pipeline_tag: text-generation
library_name: transformers
---
[](https://hf.co/QuantFactory)
# QuantFactory/gemma-2-baku-2b-GGUF
This is quantized version of [rinna/gemma-2-baku-2b](https://huggingface.co/rinna/gemma-2-baku-2b) created using llama.cpp
# Original Model Card
# `Gemma 2 Baku 2B (rinna/gemma-2-baku-2b)`
# Overview
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.
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)).
| Size | Continual Pre-Training | Instruction-Tuning |
| :- | :- | :- |
| 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) |
* **Library**
The model was trained using code based on [Lightning-AI/litgpt](https://github.com/Lightning-AI/litgpt).
* **Model architecture**
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.
* **Training**
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
- [Japanese CC-100](https://huggingface.co/datasets/cc100)
- [Japanese C4](https://huggingface.co/datasets/mc4)
- [Japanese OSCAR](https://huggingface.co/datasets/oscar-corpus/colossal-oscar-1.0)
- [The Pile](https://huggingface.co/datasets/EleutherAI/pile)
- [Wikipedia](https://dumps.wikimedia.org/other/cirrussearch)
- rinna curated Japanese dataset
* **Contributors**
- [Toshiaki Wakatsuki](https://huggingface.co/t-w)
- [Xinqi Chen](https://huggingface.co/Keely0419)
- [Kei Sawada](https://huggingface.co/keisawada)
---
# Benchmarking
Please refer to [rinna's LM benchmark page](https://rinnakk.github.io/research/benchmarks/lm/index.html).
---
# How to use the model
~~~python
import transformers
import torch
model_id = "rinna/gemma-2-baku-2b"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16, "attn_implementation": "eager"},
device_map="auto"
)
output = pipeline(
"西田幾多郎は、",
max_new_tokens=256,
do_sample=True
)
print(output[0]["generated_text"])
~~~
It is recommended to use eager attention when conducting batch inference under bfloat16 precision.
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.
---
# Tokenization
The model uses the original [google/gemma-2-2b](https://huggingface.co/google/gemma-2-2b) tokenizer.
---
# How to cite
```bibtex
@misc{rinna-gemma-2-baku-2b,
title = {rinna/gemma-2-baku-2b},
author = {Wakatsuki, Toshiaki and Chen, Xinqi and Sawada, Kei},
url = {https://huggingface.co/rinna/gemma-2-baku-2b}
}
@inproceedings{sawada2024release,
title = {Release of Pre-Trained Models for the {J}apanese Language},
author = {Sawada, Kei and Zhao, Tianyu and Shing, Makoto and Mitsui, Kentaro and Kaga, Akio and Hono, Yukiya and Wakatsuki, Toshiaki and Mitsuda, Koh},
booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)},
month = {5},
year = {2024},
pages = {13898--13905},
url = {https://aclanthology.org/2024.lrec-main.1213},
note = {\url{https://arxiv.org/abs/2404.01657}}
}
```
---
# References
```bibtex
@article{gemma-2-2024,
title = {Gemma 2},
url = {https://www.kaggle.com/models/google/gemma-2},
publisher = {Kaggle},
author = {Gemma Team},
year = {2024}
}
@misc{litgpt-2023,
author = {Lightning AI},
title = {LitGPT},
howpublished = {\url{https://github.com/Lightning-AI/litgpt}},
year = {2023}
}
```
---
# License
[Gemma Terms of Use](https://ai.google.dev/gemma/terms)
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