rinna/nekomata-7b

rinna/nekomata-7b

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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

• —Tianyu Zhao
• —Akio Kaga
• —Kei Sawada

• —Release date

December 21, 2023

Benchmarking

Please refer to rinna's LM benchmark page (Sheet 20231221).

How to use the model

~~~~python import torch from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.frompretrained("rinna/nekomata-7b", trustremote_code=True)

Use GPU with bf16

model = AutoModelForCausalLM.frompretrained("rinna/nekomata-7b", devicemap="auto", trustremotecode=True, bf16=True)

Use GPU with fp16

model = AutoModelForCausalLM.frompretrained("rinna/nekomata-7b", devicemap="auto", trustremotecode=True, fp16=True)

Use CPU

model = AutoModelForCausalLM.frompretrained("rinna/nekomata-7b", devicemap="cpu", trustremotecode=True)

Automatically select device and precision

model = AutoModelForCausalLM.frompretrained("rinna/nekomata-7b", devicemap="auto", trustremotecode=True)

text = "西田幾多郎は、" tokenids = tokenizer.encode(text, addspecialtokens=False, returntensors="pt")

with torch.nograd(): outputids = model.generate( tokenids.to(model.device), maxnewtokens=200, minnewtokens=200, dosample=True, temperature=1.0, topp=0.95, padtokenid=tokenizer.padtokenid, bostokenid=tokenizer.bostokenid, eostokenid=tokenizer.eostoken_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.

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}
}

License

Tongyi Qianwen LICENSE AGREEMENT