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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)
+
+
+bilingual-gpt-neox-4b-instruction-ppo - bnb 4bits
+- Model creator: https://huggingface.co/rinna/
+- Original model: https://huggingface.co/rinna/bilingual-gpt-neox-4b-instruction-ppo/
+
+
+
+
+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-ppo
+
+![rinna-icon](./rinna.png)
+
+---
+
+# 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-instruction-sft`](https://huggingface.co/rinna/bilingual-gpt-neox-4b-instruction-sft) and has been aligned to serve as an instruction-following conversational agent.
+
+* **Model architecture**
+
+    A 36-layer, 2816-hidden-size transformer-based language model.
+
+* **RLHF**
+    
+    Following the [OpenAI InstructGPT paper](https://arxiv.org/abs/2203.02155), **Reinforcement Learning from Human Feedback** (RLHF) has been applied to aligning the model's behaviour with input instructions. Particularly, the model has been trained in two stages, i.e. **Supervised Fine-Tuning** (SFT) and [PPO](https://arxiv.org/abs/1707.06347)-based **Reinforcement Learning** (RL). 
+    * The first SFT stage produces [`rinna/bilingual-gpt-neox-4b-instruction-sft`](https://huggingface.co/rinna/bilingual-gpt-neox-4b-instruction-sft).
+    * The second RL stage produces this model.
+
+* **Reinforcement learning**
+    
+    We used [CarperAI/trlx](https://github.com/CarperAI/trlx) and its implementation of the PPO algorithm for the RL stage.
+    
+    The RL data is the subset of the following dataset and has been translated into Japanese.
+    * [Anthropic HH RLHF data](https://huggingface.co/datasets/Anthropic/hh-rlhf)
+
+* **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 PPO does not particularly improve the model's performance on the Japanese LLM benchmark in comparison with [Bilingual GPT-NeoX 4B SFT](https://huggingface.co/rinna/bilingual-gpt-neox-4b-instruction-sft), but we have seen **better conversation experience** on the PPO model than its SFT counterpart. 
+  - *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-ppo", use_fast=False)
+model = AutoModelForCausalLM.from_pretrained("rinna/bilingual-gpt-neox-4b-instruction-ppo")
+
+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-ppo,
+    title = {rinna/bilingual-gpt-neox-4b-instruction-ppo},
+    author = {Zhao, Tianyu and Sawada, Kei},
+    url = {https://huggingface.co/rinna/bilingual-gpt-neox-4b-instruction-ppo}
+}
+
+@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)
+