Quantization made by Richard Erkhov.

gemma-2-baku-2b-it - GGUF

Model creator: https://huggingface.co/rinna/
Original model: https://huggingface.co/rinna/gemma-2-baku-2b-it/

Name	Quant method	Size
gemma-2-baku-2b-it.Q2_K.gguf	Q2_K	1.15GB
gemma-2-baku-2b-it.IQ3_XS.gguf	IQ3_XS	1.22GB
gemma-2-baku-2b-it.IQ3_S.gguf	IQ3_S	1.27GB
gemma-2-baku-2b-it.Q3_K_S.gguf	Q3_K_S	1.27GB
gemma-2-baku-2b-it.IQ3_M.gguf	IQ3_M	1.3GB
gemma-2-baku-2b-it.Q3_K.gguf	Q3_K	1.36GB
gemma-2-baku-2b-it.Q3_K_M.gguf	Q3_K_M	1.36GB
gemma-2-baku-2b-it.Q3_K_L.gguf	Q3_K_L	1.44GB
gemma-2-baku-2b-it.IQ4_XS.gguf	IQ4_XS	1.47GB
gemma-2-baku-2b-it.Q4_0.gguf	Q4_0	1.52GB
gemma-2-baku-2b-it.IQ4_NL.gguf	IQ4_NL	1.53GB
gemma-2-baku-2b-it.Q4_K_S.gguf	Q4_K_S	1.53GB
gemma-2-baku-2b-it.Q4_K.gguf	Q4_K	1.59GB
gemma-2-baku-2b-it.Q4_K_M.gguf	Q4_K_M	1.59GB
gemma-2-baku-2b-it.Q4_1.gguf	Q4_1	1.64GB
gemma-2-baku-2b-it.Q5_0.gguf	Q5_0	1.75GB
gemma-2-baku-2b-it.Q5_K_S.gguf	Q5_K_S	1.75GB
gemma-2-baku-2b-it.Q5_K.gguf	Q5_K	1.79GB
gemma-2-baku-2b-it.Q5_K_M.gguf	Q5_K_M	1.79GB
gemma-2-baku-2b-it.Q5_1.gguf	Q5_1	1.87GB
gemma-2-baku-2b-it.Q6_K.gguf	Q6_K	2.0GB
gemma-2-baku-2b-it.Q8_0.gguf	Q8_0	2.59GB

Original model description:

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

`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, 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]	Gemma 2 Baku 2B Instruct [HF]

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

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 from google/gemma-2-2b-it, as follows.
```
  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

Benchmarking

Please refer to rinna's LM benchmark page.

How to use the model

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 tokenizer.

How to cite

@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

@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