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--- |
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thumbnail: https://github.com/rinnakk/japanese-pretrained-models/blob/master/rinna.png |
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datasets: |
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- mc4 |
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- wikipedia |
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- EleutherAI/pile |
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- oscar-corpus/colossal-oscar-1.0 |
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- cc100 |
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language: |
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- ja |
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- en |
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tags: |
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- qwen |
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inference: false |
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--- |
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# `rinna/nekomata-7b` |
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# Overview |
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We conduct continual pre-training of [qwen-7b](https://huggingface.co/Qwen/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. |
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* The inclusive Qwen vocabulary (vocab size > 150k) enables the model to processs Japanese texts much more efficiently than the previously released [youri series](https://huggingface.co/collections/rinna/youri-7b-654053610cb8e9d8e6289efc). |
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* The model supports a maximum sequence length of 32768. |
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The name `nekomata` comes from the Japanese word [`猫又/ねこまた/Nekomata`](https://ja.wikipedia.org/wiki/%E7%8C%AB%E5%8F%88), which is a kind of Japanese mythical creature ([`妖怪/ようかい/Youkai`](https://ja.wikipedia.org/wiki/%E5%A6%96%E6%80%AA)). |
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* **Library** |
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The model was trained using code based on [EleutherAI/gpt-neox](https://github.com/EleutherAI/gpt-neox). |
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* **Model architecture** |
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A 32-layer, 4096-hidden-size transformer-based language model. Please refer to the [Qwen paper](https://arxiv.org/abs/2309.16609) for architecture details. |
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* **Continual pre-training** |
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The model was initialized with the [qwen-7b](https://huggingface.co/Qwen/Qwen-7B) model and continually trained on around **30B** tokens from a mixture of the following corpora |
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- [Japanese CC-100](http://data.statmt.org/cc-100/ja.txt.xz) |
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- [Japanese C4](https://huggingface.co/datasets/mc4) |
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- [Japanese OSCAR](https://huggingface.co/datasets/oscar-corpus/colossal-oscar-1.0) |
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- [The Pile](https://huggingface.co/datasets/EleutherAI/pile) |
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- [Wikipedia](https://dumps.wikimedia.org/other/cirrussearch) |
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- rinna curated Japanese dataset |
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* **Authors** |
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- [Tianyu Zhao](https://huggingface.co/tianyuz) |
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- [Akio Kaga](https://huggingface.co/rakaga) |
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- [Kei Sawada](https://huggingface.co/keisawada) |
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--- |
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# Benchmarking |
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Please refer to [rinna's LM benchmark page](https://rinnakk.github.io/research/benchmarks/lm/index.html). |
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--- |
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# How to use the model |
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~~~~python |
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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("rinna/nekomata-7b", trust_remote_code=True) |
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# Use GPU with bf16 |
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# model = AutoModelForCausalLM.from_pretrained("rinna/nekomata-7b", device_map="auto", trust_remote_code=True, bf16=True) |
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# Use GPU with fp16 |
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# model = AutoModelForCausalLM.from_pretrained("rinna/nekomata-7b", device_map="auto", trust_remote_code=True, fp16=True) |
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# Use CPU |
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# model = AutoModelForCausalLM.from_pretrained("rinna/nekomata-7b", device_map="cpu", trust_remote_code=True) |
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# Automatically select device and precision |
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model = AutoModelForCausalLM.from_pretrained("rinna/nekomata-7b", device_map="auto", trust_remote_code=True) |
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text = "西田幾多郎は、" |
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token_ids = tokenizer.encode(text, add_special_tokens=False, return_tensors="pt") |
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with torch.no_grad(): |
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output_ids = model.generate( |
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token_ids.to(model.device), |
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max_new_tokens=200, |
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min_new_tokens=200, |
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do_sample=True, |
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temperature=1.0, |
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top_p=0.95, |
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pad_token_id=tokenizer.pad_token_id, |
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bos_token_id=tokenizer.bos_token_id, |
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eos_token_id=tokenizer.eos_token_id |
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) |
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output = tokenizer.decode(output_ids.tolist()[0]) |
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print(output) |
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~~~~ |
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--- |
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# Tokenization |
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The model uses the original Qwen tokenizer. It augments the [`cl100k` tiktoken tokenizer](https://github.com/openai/tiktoken) and has a vocabulary size of 151,936. The inclusive vocabulary helps the model to reach a better tokenization efficiency, especially for Japanese texts. |
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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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| Tokenizer | Japanese | English | Multilingual | |
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| --- | --- | --- | --- | |
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| Qwen | 0.24 | 0.27 | 0.27 | |
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| llama-2 | 0.40 | 0.29 | 0.36 | |
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--- |
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# How to cite |
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~~~ |
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@misc{RinnaNekomata7b, |
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url={https://huggingface.co/rinna/nekomata-7b}, |
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title={rinna/nekomata-7b}, |
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author={Zhao, Tianyu and Kaga, Akio and Sawada, Kei} |
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} |
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~~~ |
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--- |
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# Citations |
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~~~ |
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@software{gpt-neox-library, |
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title = {{GPT-NeoX: Large Scale Autoregressive Language Modeling in PyTorch}}, |
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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}, |
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url = {https://www.github.com/eleutherai/gpt-neox}, |
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doi = {10.5281/zenodo.5879544}, |
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month = {8}, |
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year = {2021}, |
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version = {0.0.1}, |
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} |
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~~~ |
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--- |
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# License |
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[Tongyi Qianwen LICENSE AGREEMENT](https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT) |
