README.md

---
license: mit
language:
- ja
---

# Sarashina1-7B

This repository provides Japanese language models trained by [SB Intuitions](https://www.sbintuitions.co.jp/).


## How to use

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, set_seed
 
model = AutoModelForCausalLM.from_pretrained("sbintuitions/sarashina1-7b", torch_dtype=torch.float16, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("sbintuitions/sarashina1-7b")
# If you want to use slow tokenizer
# tokenizer = AutoTokenizer.from_pretrained("sbintuitions/sarashina1-7b", use_fast=False, revision="slow-tokenizer")
generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
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 | Training 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)