File size: 5,569 Bytes
86ed482 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
gemma-2-baku-2b-it - bnb 4bits
- Model creator: https://huggingface.co/rinna/
- Original model: https://huggingface.co/rinna/gemma-2-baku-2b-it/
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](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.
**ORPO** 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)
|