QuantFactory/gemma-2-baku-2b-it-GGUF

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 basemodelrelation: merge pipelinetag: text-generation libraryname: transformers


QuantFactory/gemma-2-baku-2b-it-GGUF

This is quantized version of rinna/gemma-2-baku-2b-it created using llama.cpp

Original Model Card

Gemma 2 Baku 2B Instruct (rinna/gemma-2-baku-2b-it)

[image]

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.

• —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.

~~~~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
• —Toshiaki Wakatsuki
• —Kei Sawada

Benchmarking

Please refer to rinna's LM benchmark page.

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.frompretrained(modelid) model = AutoModelForCausalLM.frompretrained( modelid, devicemap="cuda", torchdtype=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.applychattemplate(chat, tokenize=False, addgenerationprompt=True)

inputids = tokenizer.encode(prompt, addspecialtokens=False, returntensors="pt").to(model.device) outputs = model.generate( inputids, maxnew_tokens=512, )

response = tokenizer.decode(outputs[0][inputids.shape[-1]:], skipspecial_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.scaleddotproduct_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