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
QuantFactory/gemma-2-baku-2b-GGUF
This is quantized version of 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 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, which is a kind of Japanese mythical creature (妖怪/ようかい/Youkai).
| Size | Continual Pre-Training | Instruction-Tuning |
|---|---|---|
| 2B | Gemma 2 Baku 2B [HF] | Gemma 2 Baku 2B Instruct [HF] |
Library
The model was trained using code based on Lightning-AI/litgpt.
Model architecture
A 26-layer, 2304-hidden-size transformer-based language model. Please refer to the Gemma 2 Model Card for detailed information on the model's architecture.
Training
The model was initialized with the google/gemma-2-2b model and continually trained on around 80B tokens from a mixture of the following corpora
- Japanese CC-100
- Japanese C4
- Japanese OSCAR
- The Pile
- Wikipedia
- rinna curated Japanese dataset
Contributors
Benchmarking
Please refer to rinna's LM benchmark page.
How to use the model
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 tokenizer.
How to cite
@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
@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}
}