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---
thumbnail: https://github.com/rinnakk/japanese-pretrained-models/blob/master/rinna.png
license: gemma
language:
- ja
- en
tags:
- gemma2
- conversational
base_model:
- google/gemma-2-2b
- google/gemma-2-2b-it
- rinna/gemma-2-baku-2b
base_model_relation: merge
pipeline_tag: text-generation
library_name: transformers
---
[](https://hf.co/QuantFactory)
# QuantFactory/gemma-2-baku-2b-it-GGUF
This is quantized version of [rinna/gemma-2-baku-2b-it](https://huggingface.co/rinna/gemma-2-baku-2b-it) created using llama.cpp
# Original Model Card
# `Gemma 2 Baku 2B Instruct (rinna/gemma-2-baku-2b-it)`
# Overview
The model is an instruction-tuned variant of [rinna/gemma-2-baku-2b](https://huggingface.co/rinna/gemma-2-baku-2b), utilizing Chat Vector and Odds Ratio Preference Optimization (ORPO) for fine-tuning. It adheres to the gemma-2 chat format.
| 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) |
* **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**
**Model merging.** The base model was endowed with instruction-following capabilities through a chat vector addition process. The chat vector was derived by subtracting the parameter vectors of [google/gemma-2-2b](https://huggingface.co/google/gemma-2-2b) from [google/gemma-2-2b-it](https://huggingface.co/google/gemma-2-2b-it), as follows.
~~~~text
rinna/gemma-2-baku-2b + 1.0 * (google/gemma-2-2b-it - google/gemma-2-2b)
~~~~
During this process, the embedding layer was excluded during the subtraction and addition of parameter vectors.
**OPRO** was applied using a subset of the following dataset to further refine the performance of the merged model.
- rinna's internal dataset
* **Contributors**
- [Xinqi Chen](https://huggingface.co/Keely0419)
- [Toshiaki Wakatsuki](https://huggingface.co/t-w)
- [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
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "rinna/gemma-2-baku-2b-it"
dtype = torch.bfloat16
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="cuda",
torch_dtype=dtype,
attn_implementation="eager",
)
chat = [
{ "role": "user", "content": "西田幾多郎とはどんな人物ですか？" },
]
prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
input_ids = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt").to(model.device)
outputs = model.generate(
input_ids,
max_new_tokens=512,
)
response = tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True)
print(response)
~~~~
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-it](https://huggingface.co/google/gemma-2-2b-it) tokenizer.
---
# How to cite
```bibtex
@misc{rinna-gemma-2-baku-2b-it,
title = {rinna/gemma-2-baku-2b-it},
author = {Chen, Xinqi and Wakatsuki, Toshiaki and Sawada, Kei},
url = {https://huggingface.co/rinna/gemma-2-baku-2b-it}
}
@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}
}
@article{huang2023chat,
title = {Chat Vector: A Simple Approach to Equip LLMs with Instruction Following and Model Alignment in New Languages},
author = {Huang, Shih-Cheng and Li, Pin-Zu and Hsu, Yu-Chi and Chen, Kuang-Ming and Lin, Yu Tung and Hsiao, Shih-Kai and Tzong-Han Tsai, Richard and Lee, Hung-yi},
year = {2023},
url = {https://arxiv.org/abs/2310.04799}
}
@article{hong2024orpo,
title = {ORPO: Monolithic Preference Optimization without Reference Model},
author = {Hong, Jiwoo and Lee, Noah and Thorne, James},
year = {2024},
url = {https://arxiv.org/abs/2403.07691}
}
```
---
# License
[Gemma Terms of Use](https://ai.google.dev/gemma/terms)
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