update

README.md

@@ -1,6 +1,8 @@
 ---
 thumbnail: https://github.com/rinnakk/japanese-pretrained-models/blob/master/rinna.png
 license: mit
+datasets:
+- Anthropic/hh-rlhf
 language:
 - ja
 - en
@@ -9,12 +11,162 @@ inference: false
 
 # bilingual-gpt-neox-4b-instruction-sft
 
+![rinna-icon](./rinna.png)
+
 ---
 # Update
 
+- **2023/08/02** We uploaded the newly trained `rinna/bilingual-gpt-neox-4b-instruction-sft` with the MIT licnese.
+  - 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 has a more open license and 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 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 |
+
+* **Authors**
+    
+    [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-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.**
+
+---
+
 # Licenese
 [The MIT license](https://opensource.org/licenses/MIT)

config.json

@@ -0,0 +1,27 @@
+{
+  "architectures": [
+    "GPTNeoXForCausalLM"
+  ],
+  "attention_dropout": 0.1,
+  "bos_token_id": 2,
+  "classifier_dropout": 0.1,
+  "eos_token_id": 3,
+  "hidden_act": "gelu",
+  "hidden_dropout": 0.1,
+  "hidden_size": 2816,
+  "initializer_range": 0.02,
+  "intermediate_size": 11264,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 2048,
+  "model_type": "gpt_neox",
+  "num_attention_heads": 22,
+  "num_hidden_layers": 36,
+  "rope_scaling": null,
+  "rotary_emb_base": 10000,
+  "rotary_pct": 1.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "use_cache": true,
+  "use_parallel_residual": false,
+  "vocab_size": 65536
+}

pytorch_model.bin

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+size 7592400645

spiece.model

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+size 1341798

tokenizer_config.json

@@ -0,0 +1 @@
+{"eos_token": "</s>", "unk_token": "[UNK]", "pad_token": "[PAD]", "extra_ids": 0, "additional_special_tokens": [], "sp_model_kwargs": {}, "bos_token": "<s>", "cls_token": "[CLS]", "sep_token": "[SEP]", "mask_token": "[MASK]", "do_lower_case": false, "tokenizer_class": "T5Tokenizer"}