| Quantization made by Richard Erkhov. | |
| [Github](https://github.com/RichardErkhov) | |
| [Discord](https://discord.gg/pvy7H8DZMG) | |
| [Request more models](https://github.com/RichardErkhov/quant_request) | |
| bilingual-gpt-neox-4b-instruction-sft - bnb 8bits | |
| - Model creator: https://huggingface.co/rinna/ | |
| - Original model: https://huggingface.co/rinna/bilingual-gpt-neox-4b-instruction-sft/ | |
| Original model description: | |
| --- | |
| thumbnail: https://github.com/rinnakk/japanese-pretrained-models/blob/master/rinna.png | |
| license: mit | |
| datasets: | |
| - Anthropic/hh-rlhf | |
| language: | |
| - ja | |
| - en | |
| inference: false | |
| base_model: rinna/bilingual-gpt-neox-4b | |
| --- | |
| # bilingual-gpt-neox-4b-instruction-sft | |
|  | |
| --- | |
| # Update | |
| - **2023/08/02** We uploaded the newly trained `rinna/bilingual-gpt-neox-4b-instruction-sft` with the MIT license. | |
| - Please refrain from using the previous model released on 2023/07/31 for commercial purposes if you have already downloaded it. | |
| - 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. | |
| - For reference, we provide the MD5 checksum values for the `pytorch_model.bin` files of the previous and current models. | |
| - 2023/07/31 model: `edf190a323c0ae63f71476700fb0b462` | |
| - 2023/08/02 model: `de72aa5b66beee7b65783c96f687d186` | |
| - **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. | |
| --- | |
| # Overview | |
| This repository provides an English-Japanese bilingual GPT-NeoX model of 3.8 billion parameters. | |
| 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. | |
| * **Model architecture** | |
| A 36-layer, 2816-hidden-size transformer-based language model. | |
| * **Fine-tuning** | |
| The fine-tuning data is the subset of the following datasets. | |
| * [Anthropic HH RLHF data](https://huggingface.co/datasets/Anthropic/hh-rlhf) and its Japanese translation | |
| * [FLAN Instruction Tuning data](https://github.com/google-research/FLAN) and its Japanese translation | |
| * **Model Series** | |
| | Variant | Link | | |
| | :-- | :--| | |
| | Bilingual 4B MiniGPT4 | https://huggingface.co/rinna/bilingual-gpt-neox-4b-minigpt4 | | |
| | Bilingual 4B PPO | https://huggingface.co/rinna/bilingual-gpt-neox-4b-instruction-ppo | | |
| | Bilingual 4B SFT | https://huggingface.co/rinna/bilingual-gpt-neox-4b-instruction-sft | | |
| | Bilingual 4B 8K | https://huggingface.co/rinna/bilingual-gpt-neox-4b-8k | | |
| | Bilingual 4B | https://huggingface.co/rinna/bilingual-gpt-neox-4b | | |
| | Japanese 3.6B PPO | https://huggingface.co/rinna/japanese-gpt-neox-3.6b-instruction-ppo | | |
| | Japanese 3.6B SFT-v2 | https://huggingface.co/rinna/japanese-gpt-neox-3.6b-instruction-sft-v2 | | |
| | Japanese 3.6B SFT | https://huggingface.co/rinna/japanese-gpt-neox-3.6b-instruction-sft | | |
| | Japanese 3.6B | https://huggingface.co/rinna/japanese-gpt-neox-3.6b | | |
| * **Contributors** | |
| [Tianyu Zhao](https://huggingface.co/tianyuz) and [Kei Sawada](https://huggingface.co/keisawada) | |
| --- | |
| # Benchmarking | |
| 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. | |
| - *The 4-task average accuracy is based on results of JCommonsenseQA, JNLI, MARC-ja, and JSQuAD.* | |
| - *The 6-task average accuracy is based on results of JCommonsenseQA, JNLI, MARC-ja, JSQuAD, XWinograd, and JAQKET-v2.* | |
| | Model | 4-task average accuracy | 6-task average accuracy | | |
| | :-- | :-- | :-- | | |
| | bilingual-gpt-neox-4b-instruction-ppo | 61.01 | 61.16 | | |
| | **bilingual-gpt-neox-4b-instruction-sft** | **61.02** | **61.69** | | |
| | bilingual-gpt-neox-4b | 56.12 | 51.83 | | |
| | japanese-gpt-neox-3.6b-instruction-ppo | 59.86 | 60.07 | | |
| | japanese-gpt-neox-3.6b | 55.07 | 50.32 | | |
| --- | |
| # I/O Format | |
| A special format has been adopted to construct inputs. | |
| * An input prompt is formatted as a conversation between `ユーザー` and `システム`. | |
| * 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？"`). | |
| * The input prompt should be ended with `"システム: "` to acknowledge the model to generate a response. | |
| * All the utterances in the input prompt should be separated by a newline `\n`. | |
| Following is an example to construct input from a conversation. | |
| ~~~python | |
| prompt = [ | |
| { | |
| "speaker": "ユーザー", | |
| "text": "Hello, you are an assistant that helps me learn Japanese." | |
| }, | |
| { | |
| "speaker": "システム", | |
| "text": "Sure, what can I do for you?" | |
| }, | |
| { | |
| "speaker": "ユーザー", | |
| "text": "VRはなんですか。" | |
| } | |
| ] | |
| prompt = [ | |
| f"{uttr['speaker']}: {uttr['text']}" | |
| for uttr in prompt | |
| ] | |
| prompt = "\n".join(prompt) | |
| prompt = ( | |
| prompt | |
| + "\n" | |
| + "システム: " | |
| ) | |
| print(prompt) | |
| """ | |
| ユーザー: Hello, you are an assistant that helps me learn Japanese. | |
| システム: Sure, what can I do for you? | |
| ユーザー: VRはなんですか。 | |
| システム: | |
| """ | |
| ~~~ | |
| --- | |
| # How to use the model | |
| **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. | |
| ~~~~python | |
| import torch | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| tokenizer = AutoTokenizer.from_pretrained("rinna/bilingual-gpt-neox-4b-instruction-sft", use_fast=False) | |
| model = AutoModelForCausalLM.from_pretrained("rinna/bilingual-gpt-neox-4b-instruction-sft") | |
| if torch.cuda.is_available(): | |
| model = model.to("cuda") | |
| token_ids = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt") | |
| with torch.no_grad(): | |
| output_ids = model.generate( | |
| token_ids.to(model.device), | |
| max_new_tokens=512, | |
| do_sample=True, | |
| temperature=1.0, | |
| top_p=0.85, | |
| 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][token_ids.size(1):]) | |
| print(output) | |
| """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>""" | |
| ~~~~ | |
| --- | |
| # Tokenization | |
| The model uses a [sentencepiece](https://github.com/google/sentencepiece)-based tokenizer. | |
| * The tokenizer has a vocabulary size of 65,536. | |
| * It uses *byte fallback* to decompose unknown text pieces into UTF-8 byte pieces to avoid producing `<UNK>` tokens. | |
| * It can recognize *consecutive whitespaces*, *newlines*, and *tabs* to handle structured texts better. | |
| * We turned off the default behaviour of prepending leading whitespace because it is not beneficial for processing Japanese. | |
| * 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`). | |
| * This decision trades the English processing efficiency for a unified way to treat whitespaces. | |
| * It leads to a significantly lower loss of next token prediction on English data because whitespaces are easy to predict. | |
| * **Don't forget to set `use_fast=False` to make the above features function correctly.** | |
| --- | |
| # How to cite | |
| ```bibtex | |
| @misc{rinna-bilingual-gpt-neox-4b-instruction-sft, | |
| title = {rinna/bilingual-gpt-neox-4b-instruction-sft}, | |
| author = {Zhao, Tianyu and Sawada, Kei}, | |
| url = {https://huggingface.co/rinna/bilingual-gpt-neox-4b-instruction-sft} | |
| } | |
| @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}} | |
| } | |
| ``` | |
| --- | |
| # Licenese | |
| [The MIT license](https://opensource.org/licenses/MIT) | |