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--- |
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thumbnail: https://github.com/rinnakk/japanese-pretrained-models/blob/master/rinna.png |
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license: mit |
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datasets: |
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- Anthropic/hh-rlhf |
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language: |
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- ja |
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- en |
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inference: false |
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--- |
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# bilingual-gpt-neox-4b-instruction-sft |
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--- |
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# Update |
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- **2023/08/02** We uploaded the newly trained `rinna/bilingual-gpt-neox-4b-instruction-sft` with the MIT license. |
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- Please refrain from using the previous model released on 2023/07/31 for commercial purposes if you have already downloaded it. |
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- The new model released on 2023/08/02 is built from datasets with less strict licenses and has better evaluation performance, so we suggest using the new model. |
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- For reference, we provide the MD5 checksum values for the `pytorch_model.bin` files of the previous and current models. |
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- 2023/07/31 model: `edf190a323c0ae63f71476700fb0b462` |
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- 2023/08/02 model: `de72aa5b66beee7b65783c96f687d186` |
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- **2023/07/31** In the previously released `rinna/bilingual-gpt-neox-4b-instruction-sft`, we found that part of the training data (i.e. Openchat ShareGPT4 and WizardLM) have a non-commercial license, and thus it does not comply with **the MIT license**. We decided to remove the previous version and build a new SFT model from datasets with less strict licenses. The new model will be uploaded in a few days. We sincerely apologize for our careless mistake. |
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--- |
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# Overview |
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This repository provides an English-Japanese bilingual GPT-NeoX model of 3.8 billion parameters. |
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The model is based on [`rinna/bilingual-gpt-neox-4b`](https://huggingface.co/rinna/bilingual-gpt-neox-4b) and has been finetuned to serve as an instruction-following conversational agent. |
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* **Model architecture** |
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A 36-layer, 2816-hidden-size transformer-based language model. |
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* **Fine-tuning** |
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The fine-tuning data is the subset of the following datasets. |
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* [Anthropic HH RLHF data](https://huggingface.co/datasets/Anthropic/hh-rlhf) and its Japanese translation |
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* [FLAN Instruction Tuning data](https://github.com/google-research/FLAN) and its Japanese translation |
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* **Model Series** |
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| Variant | Link | |
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| :-- | :--| |
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| Bilingual 4B MiniGPT4 | https://huggingface.co/rinna/bilingual-gpt-neox-4b-minigpt4 | |
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| Bilingual 4B PPO | https://huggingface.co/rinna/bilingual-gpt-neox-4b-instruction-ppo | |
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| Bilingual 4B SFT | https://huggingface.co/rinna/bilingual-gpt-neox-4b-instruction-sft | |
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| Bilingual 4B 8K | https://huggingface.co/rinna/bilingual-gpt-neox-4b-8k | |
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| Bilingual 4B | https://huggingface.co/rinna/bilingual-gpt-neox-4b | |
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| Japanese 3.6B PPO | https://huggingface.co/rinna/japanese-gpt-neox-3.6b-instruction-ppo | |
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| Japanese 3.6B SFT-v2 | https://huggingface.co/rinna/japanese-gpt-neox-3.6b-instruction-sft-v2 | |
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| Japanese 3.6B SFT | https://huggingface.co/rinna/japanese-gpt-neox-3.6b-instruction-sft | |
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| Japanese 3.6B | https://huggingface.co/rinna/japanese-gpt-neox-3.6b | |
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* **Contributors** |
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[Tianyu Zhao](https://huggingface.co/tianyuz) and [Kei Sawada](https://huggingface.co/keisawada) |
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--- |
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# Benchmarking |
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Our evaluation experiments suggest that the bilingual-gpt-neox-4b-instruction-sft model performs slightly better than the previous [Japanese GPT-NeoX 3.6B PPO](https://huggingface.co/rinna/japanese-gpt-neox-3.6b-instruction-ppo) in Japanese tasks. |
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- *The 4-task average accuracy is based on results of JCommonsenseQA, JNLI, MARC-ja, and JSQuAD.* |
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- *The 6-task average accuracy is based on results of JCommonsenseQA, JNLI, MARC-ja, JSQuAD, XWinograd, and JAQKET-v2.* |
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| Model | 4-task average accuracy | 6-task average accuracy | |
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| :-- | :-- | :-- | |
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| bilingual-gpt-neox-4b-instruction-ppo | 61.01 | 61.16 | |
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| **bilingual-gpt-neox-4b-instruction-sft** | **61.02** | **61.69** | |
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| bilingual-gpt-neox-4b | 56.12 | 51.83 | |
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| japanese-gpt-neox-3.6b-instruction-ppo | 59.86 | 60.07 | |
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| japanese-gpt-neox-3.6b | 55.07 | 50.32 | |
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--- |
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# I/O Format |
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A special format has been adopted to construct inputs. |
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* An input prompt is formatted as a conversation between `ユーザー` and `システム`. |
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* Each input utterance consists of (1) its speaker (`"ユーザー"` or `"システム"`), (2) a colon (`":"`), (3) a whitespace (`" "`), and (4) utterance text (e.g. `"世界で一番高い山は？"`). |
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* The input prompt should be ended with `"システム: "` to acknowledge the model to generate a response. |
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* All the utterances in the input prompt should be separated by a newline `\n`. |
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Following is an example to construct input from a conversation. |
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~~~python |
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prompt = [ |
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{ |
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"speaker": "ユーザー", |
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"text": "Hello, you are an assistant that helps me learn Japanese." |
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}, |
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{ |
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"speaker": "システム", |
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"text": "Sure, what can I do for you?" |
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}, |
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{ |
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"speaker": "ユーザー", |
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"text": "VRはなんですか。" |
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} |
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] |
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prompt = [ |
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f"{uttr['speaker']}: {uttr['text']}" |
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for uttr in prompt |
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] |
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prompt = "\n".join(prompt) |
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prompt = ( |
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prompt |
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+ "\n" |
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+ "システム: " |
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) |
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print(prompt) |
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""" |
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ユーザー: Hello, you are an assistant that helps me learn Japanese. |
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システム: Sure, what can I do for you? |
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ユーザー: VRはなんですか。 |
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システム: |
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""" |
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~~~ |
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--- |
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# How to use the model |
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**Notice:** Since the model is **sensitive to decoding hyper-parameters** (e.g. `temperature`, `top_p`, `top_k`, `repetition_penalty`), it is suggested to explore the best setting for your task. |
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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/bilingual-gpt-neox-4b-instruction-sft", use_fast=False) |
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model = AutoModelForCausalLM.from_pretrained("rinna/bilingual-gpt-neox-4b-instruction-sft") |
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if torch.cuda.is_available(): |
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model = model.to("cuda") |
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token_ids = tokenizer.encode(prompt, 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=512, |
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do_sample=True, |
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temperature=1.0, |
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top_p=0.85, |
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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][token_ids.size(1):]) |
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print(output) |
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"""VRとはVirtual Realityの略で、仮想現実とも呼ばれます。これは、コンピューターを使用して仮想世界を作り出し、仮想世界上でコンピューターのゲームや仮想世界を体験するための技術です。この技術は、コンピューターやモバイ ルデバイスの進歩によって、2015年以降、ますます普及しています。VRは、ゲームや仮想世界、その他のアプリケー ションなどのさまざまな分野で、コンピューターと人間の相互作用の新しい方法を提供しています。</s>""" |
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~~~~ |
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--- |
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# Tokenization |
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The model uses a [sentencepiece](https://github.com/google/sentencepiece)-based tokenizer. |
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* The tokenizer has a vocabulary size of 65,536. |
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* It uses *byte fallback* to decompose unknown text pieces into UTF-8 byte pieces to avoid producing `<UNK>` tokens. |
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* It can recognize *consecutive whitespaces*, *newlines*, and *tabs* to handle structured texts better. |
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* We turned off the default behaviour of prepending leading whitespace because it is not beneficial for processing Japanese. |
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* Specifically, single whitespace is always processed as one token so that any English word won't have a preceding whitespace like in many other tokenizers (e.g. `_Hello`). |
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* This decision trades the English processing efficiency for a unified way to treat whitespaces. |
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* It leads to a significantly lower loss of next token prediction on English data because whitespaces are easy to predict. |
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* **Don't forget to set `use_fast=False` to make the above features function correctly.** |
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--- |
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# How to cite |
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~~~ |
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@misc{rinna-bilingual-gpt-neox-4b-instruction-sft, |
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title = {rinna/bilingual-gpt-neox-4b-instruction-sft}, |
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author = {Zhao, Tianyu and Sawada, Kei}, |
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url = {https://huggingface.co/rinna/bilingual-gpt-neox-4b-instruction-sft}, |
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} |
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@inproceedings{sawada2024release, |
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title = {Release of Pre-Trained Models for the {J}apanese Language}, |
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author = {Sawada, Kei and Zhao, Tianyu and Shing, Makoto and Mitsui, Kentaro and Kaga, Akio and Hono, Yukiya and Wakatsuki, Toshiaki and Mitsuda, Koh}, |
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booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)}, |
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month = {5}, |
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year = {2024}, |
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url = {https://arxiv.org/abs/2404.01657}, |
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} |
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~~~ |
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--- |
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# Licenese |
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[The MIT license](https://opensource.org/licenses/MIT) |
