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
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)
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}
}