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README.md

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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
+
+---
+
+[![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](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)`
+
+![rinna-icon](./rinna.png)
+
+# 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)