thumbnail: https://github.com/rinnakk/japanese-pretrained-models/blob/master/rinna.png
datasets:
- mc4
- wikipedia
- EleutherAI/pile
- oscar-corpus/colossal-oscar-1.0
- cc100
language:
- ja
- en
tags:
- qwen
inference: false
license: other
license_name: tongyi-qianwen-license-agreement
license_link: >-
https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT
base_model: Qwen/Qwen-7B
rinna/nekomata-7b
Overview
We conduct continual pre-training of qwen-7b on 30B tokens from a mixture of Japanese and English datasets. The continual pre-training significantly improves the model's performance on Japanese tasks. It also enjoys the following great features provided by the original Qwen model.
- The inclusive Qwen vocabulary (vocab size > 150k) enables the model to processs Japanese texts much more efficiently than the previously released youri series.
- The model supports a maximum sequence length of 32768.
The name nekomata comes from the Japanese word 猫又/ねこまた/Nekomata, which is a kind of Japanese mythical creature (妖怪/ようかい/Youkai).
Library
The model was trained using code based on EleutherAI/gpt-neox.
Model architecture
A 32-layer, 4096-hidden-size transformer-based language model. Please refer to the Qwen paper for architecture details.
Continual pre-training
The model was initialized with the qwen-7b model and continually trained on around 30B tokens from a mixture of the following corpora
- Japanese CC-100
- Japanese C4
- Japanese OSCAR
- The Pile
- Wikipedia
- rinna curated Japanese dataset
Contributors
Benchmarking
Please refer to rinna's LM benchmark page.
How to use the model
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("rinna/nekomata-7b", trust_remote_code=True)
# Use GPU with bf16
# model = AutoModelForCausalLM.from_pretrained("rinna/nekomata-7b", device_map="auto", trust_remote_code=True, bf16=True)
# Use GPU with fp16
# model = AutoModelForCausalLM.from_pretrained("rinna/nekomata-7b", device_map="auto", trust_remote_code=True, fp16=True)
# Use CPU
# model = AutoModelForCausalLM.from_pretrained("rinna/nekomata-7b", device_map="cpu", trust_remote_code=True)
# Automatically select device and precision
model = AutoModelForCausalLM.from_pretrained("rinna/nekomata-7b", device_map="auto", trust_remote_code=True)
text = "西田幾多郎は、"
token_ids = tokenizer.encode(text, add_special_tokens=False, return_tensors="pt")
with torch.no_grad():
output_ids = model.generate(
token_ids.to(model.device),
max_new_tokens=200,
min_new_tokens=200,
do_sample=True,
temperature=1.0,
top_p=0.95,
pad_token_id=tokenizer.pad_token_id,
bos_token_id=tokenizer.bos_token_id,
eos_token_id=tokenizer.eos_token_id
)
output = tokenizer.decode(output_ids.tolist()[0])
print(output)
Tokenization
The model uses the original Qwen tokenizer. It augments the cl100k tiktoken tokenizer and has a vocabulary size of 151,936. The inclusive vocabulary helps the model to reach a better tokenization efficiency, especially for Japanese texts.
We compared the Qwen tokenizer (as used in nekomata) and the llama-2 tokenizer (as used in youri) on different text collections and found that the Qwen tokenizer achieves a much better byte2token rate (i.e. the average number of tokens produced from 1 byte of text) as following. A lower byte2token rate indicates a better tokenization efficiency.
| Tokenizer | Japanese | English | Multilingual |
|---|---|---|---|
| Qwen | 0.24 | 0.27 | 0.27 |
| llama-2 | 0.40 | 0.29 | 0.36 |
How to cite
@misc{rinna-nekomata-7b,
title = {rinna/nekomata-7b},
author = {Zhao, Tianyu and Kaga, Akio and Sawada, Kei},
url = {https://huggingface.co/rinna/nekomata-7b}
}
@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
@software{gpt-neox-library,
title = {{GPT}-{N}eo{X}: Large Scale Autoregressive Language Modeling in {P}y{T}orch},
author = {Andonian, Alex and Anthony, Quentin and Biderman, Stella and Black, Sid and Gali, Preetham and Gao, Leo and Hallahan, Eric and Levy-Kramer, Josh and Leahy, Connor and Nestler, Lucas and Parker, Kip and Pieler, Michael and Purohit, Shivanshu and Songz, Tri and Phil, Wang and Weinbach, Samuel},
doi = {10.5281/zenodo.5879544},
month = {8},
year = {2021},
version = {0.0.1},
url = {https://www.github.com/eleutherai/gpt-neox}
}