rinna_-_nekomata-7b-gguf

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nekomata-7b - GGUF

• Model creator: https://huggingface.co/rinna/
• Original model: https://huggingface.co/rinna/nekomata-7b/

Original model description:

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

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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

  • Tianyu Zhao
  • Akio Kaga
  • Kei Sawada

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Benchmarking

Please refer to rinna's LM benchmark page.

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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)

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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.

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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}}
}

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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}
}

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License

Tongyi Qianwen LICENSE AGREEMENT