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README.md

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+Quantization made by Richard Erkhov.
+
+[Github](https://github.com/RichardErkhov)
+
+[Discord](https://discord.gg/pvy7H8DZMG)
+
+[Request more models](https://github.com/RichardErkhov/quant_request)
+
+
+japanese-gpt-neox-3.6b-instruction-sft - bnb 4bits
+- Model creator: https://huggingface.co/rinna/
+- Original model: https://huggingface.co/rinna/japanese-gpt-neox-3.6b-instruction-sft/
+
+
+
+
+Original model description:
+---
+language: ja
+thumbnail: https://github.com/rinnakk/japanese-pretrained-models/blob/master/rinna.png
+tags:
+- gpt_neox
+- text-generation
+- lm
+- nlp
+license: mit
+datasets:
+- Anthropic/hh-rlhf
+- stanfordnlp/SHP
+inference: false
+base_model: rinna/japanese-gpt-neox-3.6b
+---
+
+# japanese-gpt-neox-3.6b-instruction-sft
+
+![rinna-icon](./rinna.png)
+
+# Overview
+This repository provides a Japanese GPT-NeoX model of 3.6 billion parameters. The model is based on [`rinna/japanese-gpt-neox-3.6b`](https://huggingface.co/rinna/japanese-gpt-neox-3.6b) and has been finetuned to serve as an instruction-following conversational agent.
+
+* **Model architecture**
+    
+    A 36-layer, 2816-hidden-size transformer-based language model.
+
+* **Finetuning**
+    
+    The finetuning data is the subset of the following datasets and has been translated into Japanese.
+    * [Anthropic HH RLHF data](https://huggingface.co/datasets/Anthropic/hh-rlhf)
+    * [FLAN Instruction Tuning data](https://github.com/google-research/FLAN)
+    * [Stanford Human Preferences Dataset](https://huggingface.co/datasets/stanfordnlp/SHP)
+
+    The data will **not** be released.
+
+* **Model Series**
+
+    | Variant | Link |
+    | :-- | :--|
+    | 3.6B PPO | https://huggingface.co/rinna/japanese-gpt-neox-3.6b-instruction-ppo |
+    | 3.6B SFT-v2 | https://huggingface.co/rinna/japanese-gpt-neox-3.6b-instruction-sft-v2 |
+    | 3.6B SFT | https://huggingface.co/rinna/japanese-gpt-neox-3.6b-instruction-sft |
+    | 3.6B pretrained | https://huggingface.co/rinna/japanese-gpt-neox-3.6b |
+    
+* **Contributors**
+    
+    [Tianyu Zhao](https://huggingface.co/tianyuz) and [Kei Sawada](https://huggingface.co/keisawada)
+
+# 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.
+* Since the model's tokenizer does not recognize `"\n"`, a special newline symbol `"<NL>"` is used instead.
+* All the newlines in input and output utterances should be replaced with `"<NL>"`.
+* All the utterances in the input prompt should be separated by `"<NL>"`.
+
+Following is an example to construct an input from a conversation.
+~~~python
+prompt = [
+    {
+        "speaker": "ユーザー",
+        "text": "日本のおすすめの観光地を教えてください。"
+    },
+    {
+        "speaker": "システム",
+        "text": "どの地域の観光地が知りたいですか？"
+    },
+    {
+        "speaker": "ユーザー",
+        "text": "渋谷の観光地を教えてください。"
+    }
+]
+prompt = [
+    f"{uttr['speaker']}: {uttr['text']}"
+    for uttr in prompt
+]
+prompt = "<NL>".join(prompt)
+prompt = (
+    prompt
+    + "<NL>"
+    + "システム: "
+)
+print(prompt)
+# "ユーザー: 日本のおすすめの観光地を教えてください。<NL>システム: どの地域の観光地が知りたいですか？<NL>ユーザー: 渋谷の観光地を教えてください。<NL>システム: "
+~~~
+
+# How to use the model
+
+~~~~python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("rinna/japanese-gpt-neox-3.6b-instruction-sft", use_fast=False)
+model = AutoModelForCausalLM.from_pretrained("rinna/japanese-gpt-neox-3.6b-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),
+        do_sample=True,
+        max_new_tokens=128,
+        temperature=0.7,
+        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):])
+output = output.replace("<NL>", "\n")
+print(output)
+"""分かりました。いくつかのおすすめを紹介します。
+1. ハチ公像です。ハチ公像は、日本の観光スポットの1つとして人気があります。
+2. スクランブル交差点です。多くの人々が行き交う大きな交差点で、観光客に人気のスポットです。
+3. 109です。109は、ショッピングやエンターテイメント施設です。
+4. 道玄坂です。道玄坂は、日本の商業地区である坂道です。</s>"""
+~~~~
+
+# Tokenization
+The model uses a [sentencepiece](https://github.com/google/sentencepiece)-based tokenizer.
+* The tokenizer has a vocabulary size of 32,000.
+* It uses sentencepiece's byte fallback feature to decompose unknown text pieces into UTF-8 byte pieces and to avoid producing `<UNK>` tokens.
+* sentencepiece's `--add_dummy_prefix` option was turned off so that a leading whitespace will not be prepended automatically.
+    ~~~
+    print(tokenizer.tokenize("吾輩は猫である"))
+    # ['吾', '輩', 'は', '猫', 'である']
+    # instead of ['▁', '吾', '輩', 'は', '猫', 'である'] as in rinna/japanese-gpt-1b
+    ~~~
+* sentencepiece's `--remove_extra_whitespaces` option was turned off so that leading, trailing, and duplicate whitespaces are reserved.
+    ~~~
+    print(tokenizer.tokenize("  吾輩は  猫である   "))
+    # ['▁', '▁', '吾', '輩', 'は', '▁', '▁', '猫', 'である', '▁', '▁', '▁']
+    # instead of ['▁', '吾', '輩', 'は', '▁猫', 'である'] as in rinna/japanese-gpt-1b
+    ~~~
+* Don't forget to set `use_fast=False` to make the above features function correctly.
+    ~~~
+    good_tokenizer = AutoTokenizer.from_pretrained("rinna/japanese-gpt-neox-3.6b", use_fast=False)
+    bad_tokenizer = AutoTokenizer.from_pretrained("rinna/japanese-gpt-neox-3.6b")
+
+    print(good_tokenizer.decode(good_tokenizer.encode("გამარჯობა  吾輩は  猫である   ")))
+    # 'გამარჯობა  吾輩は  猫である   </s>'
+    print(bad_tokenizer.decode(bad_tokenizer.encode("გამარჯობა  吾輩は  猫である   ")))
+    # 'გამარ[UNK]ობა 吾輩は 猫である </s>'
+    ~~~
+
+# How to cite
+```bibtex
+@misc{rinna-japanese-gpt-neox-3.6b-instruction-sft,
+    title = {rinna/japanese-gpt-neox-3.6b-instruction-sft},
+    author = {Zhao, Tianyu and Sawada, Kei},
+    url = {https://huggingface.co/rinna/japanese-gpt-neox-3.6b-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)
+