add readme

README.md

@@ -1,3 +1,68 @@
 ---
 license: mit
+language:
+- ja
 ---
+
+# Sarashina1-7B
+
+This repository provides Japanese language models trained by [SB Intuitions](https://www.sbintuitions.co.jp/).
+
+
+## How to use
+
+```
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, set_seed
+ 
+model = AutoModelForCausalLM.from_pretrained("sbintuitions/sarashina1-7b", torch_dtype=torch.float16)
+tokenizer = AutoTokenizer.from_pretrained("sbintuitions/sarashina1-7b", use_fast=False)
+generator = pipeline("text-generation", model=model, tokenizer=tokenizer, device=0)
+set_seed(123)
+ 
+text = generator(
+    "おはようございます、今日の天気は",
+    max_length=30,
+    do_sample=True,
+    pad_token_id=tokenizer.pad_token_id,
+    num_return_sequences=3,
+)
+ 
+for t in text:
+  print(t)
+ 
+# These examples are generated by sarashina1-7b parameters model
+# {'generated_text': 'おはようございます、今日の天気は晴れ!!最高気温は15度、最低気温は7度です。今日も1日頑張りましょー♪写真は、去年'}
+# {'generated_text': 'おはようございます、今日の天気は曇り:cloud:です。 雨予報なので、洗濯物は家の中へ。 :city_sunrise:の見える時間。 今日は'}
+# {'generated_text': 'おはようございます、今日の天気は、晴れ、気温も10度以上に上がるそうです、お日様が当たっていると15度くらいになると思います、朝の'}
+```
+
+## Configuration
+
+| Parameters | Vocab size | Trainning tokens |  Architecture | Position type | Layers | Hidden dim | Attention heads |
+| :-----: | :-----------: | :-------------:  | :----------- | :-----------: | :----: | :--------: | :-------------: |
+| [7B](https://huggingface.co/sbintuitions/sarashina1-7b)      | 51200         | 1.0T             | GPTNeoX      | RoPE          | 32     | 4096       | 32 |
+| [13B](https://huggingface.co/sbintuitions/sarashina1-13b)     | 51200         | 1.0T             | GPTNeoX      | RoPE          | 40     | 5120       | 40 |
+| [65B](https://huggingface.co/sbintuitions/sarashina1-65b)     | 51200         | 800B             | GPTNeoX      | RoPE          | 80     | 8192       | 64 |
+
+## Training Corpus
+
+We used a Japanese portion of the [Common Crawl corpus](https://commoncrawl.org/), which is the largest Web corpus, as our training dataset.
+To clean the training corpus, we used [CCNet](https://github.com/facebookresearch/cc_net) and [HojiChar](https://github.com/HojiChar/HojiChar).
+After cleaning, our corpus contains about 550B tokens.
+
+## Tokenization
+
+We use a [sentencepiece](https://github.com/google/sentencepiece) tokenizer with a unigram language model and byte-fallback.
+We do not apply pre-tokenization with Japanese tokenizer.
+Thus, a user may directly feed raw sentences into the tokenizer.
+
+
+## Ethical Considerations and Limitations
+Sarashina1 has not been tuned to follow an instruction yet.
+Therefore, sarashina1 might generate some meaningless sequences, some inaccurate instances or biased/objectionable outputs.
+Before using sarashina1, we would like developers to tune models based on human preferences and safety considerations.
+
+## License
+
+[MIT License](https://huggingface.co/sbintuitions/sarashina1-7b/blob/main/LICENSE)