init commit

README.md

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 ---
+language: ja
+thumbnail: https://github.com/rinnakk/japanese-pretrained-models/blob/master/rinna.png
+tags:
+- ja
+- gpt_neox
+- text-generation
+- lm
+- nlp
 license: mit
+datasets:
+- cc100
+- wikipedia
+- mc4
+inference: false
 ---
+
+# japanese-gpt-neox-3.6b
+
+![rinna-icon](./rinna.png)
+
+This repository provides a Japanese GPT-NeoX model of 3.6 billion parameters. The model was trained using code based on [EleutherAI/gpt-neox](https://github.com/EleutherAI/gpt-neox).
+
+# How to use the model
+
+~~~~python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("rinna/japanese-gpt-neox-3.6b", use_fast=False)
+model = AutoModelForCausalLM.from_pretrained("rinna/japanese-gpt-neox-3.6b")
+
+if torch.cuda.is_available():
+    model = model.to("cuda")
+
+text = "西田幾多郎は、"
+token_ids = tokenizer.encode(text, add_special_tokens=False, return_tensors="pt")
+
+with torch.no_grad():
+    output_ids = model.generate(
+        token_ids.to(model.device),
+        max_new_tokens=100,
+        min_new_tokens=100,
+        do_sample=True,
+        temperature=0.8,
+        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])
+print(output)
+"""西田幾多郎は、この「絶対矛盾的自己同一」を「世界の自己同一」と置きかえ、さらに西田哲学を出発点として「絶対無」を「世界の成立」に変え、世界と自己を一つの統一物とみなす哲学として展開する。この世界と自己は絶対矛盾的自己同一として同一の性質を有し、同じ働きをする。西田哲学においては、この世界と自己は矛盾しあうのではなく、同一の性質をもっている。この世界と自己は同一である。絶対"""
+~~~~
+
+# Model architecture
+A 36-layer, 2816-hidden-size transformer-based language model.
+
+# Training
+The model was trained on around **312.5B** tokens from [Japanese CC-100](http://data.statmt.org/cc-100/ja.txt.xz), [Japanese C4](https://huggingface.co/datasets/mc4), and [Japanese Wikipedia](https://dumps.wikimedia.org/other/cirrussearch) to optimize a traditional language modelling objective. 
+
+A final validation perplexity of **8.68** has been reached.
+
+# 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>'
+    ~~~
+
+# Licenese
+[The MIT license](https://opensource.org/licenses/MIT)

config.json

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+{
+  "architectures": [
+    "GPTNeoXForCausalLM"
+  ],
+  "bos_token_id": 2,
+  "eos_token_id": 3,
+  "hidden_act": "gelu",
+  "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,
+  "rotary_emb_base": 10000,
+  "rotary_pct": 1.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "use_cache": true,
+  "use_parallel_residual": false,
+  "vocab_size": 32000
+}

pytorch_model.bin

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+oid sha256:f97bb2681d00e08db1eb791c7c4f75c37ef0cc55591585442c680154d35d2609
+size 7365670537

spiece.model

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+oid sha256:7d78ab344146700112cd41628ac7ce54b79c0868fe0c7c201750d8237b54dbb4
+size 786216

tokenizer_config.json

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+{"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"}