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---
license: mit
language:
- ja
- en
---
# Sarashina2-13B
This repository provides large 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/sarashina2-13b", torch_dtype=torch.bfloat16, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("sbintuitions/sarashina2-13b")
# If you want to use slow tokenizer
# tokenizer = AutoTokenizer.from_pretrained("sbintuitions/sarashina2-13b", use_fast=False）
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)
```
## Configuration
| Parameters | Vocab size | Training tokens | Architecture | Position type | Layers | Hidden dim | Attention heads |
| :-----: | :-----------: | :-------------: | :------------ | :-----------: | :----: | :--------: | :-------------: |
| [7B](https://huggingface.co/sbintuitions/sarashina2-7b) | 102400 | 2.1T | Llama2 | RoPE | 32 | 4096 | 32 |
| [13B](https://huggingface.co/sbintuitions/sarashina2-13b) | 102400 | 2.1T | Llama2 | RoPE | 40 | 5120 | 40 |
| [70B](https://huggingface.co/sbintuitions/sarashina2-70b) | 102400 | 2.1T | Llama2 | RoPE | 80 | 8192 | 64 |
## Training Corpus
For our Japanese training data, 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 Japanese training data contains about 1T tokens.
For our English training data, we extracted English documents from [SlimPajama](https://huggingface.co/datasets/cerebras/SlimPajama-627B) but we removed books3 corpus due to copyright infringement.
## 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
Sarashina2 has not been tuned to follow an instruction yet.
Therefore, sarashina2 might generate some meaningless sequences, some inaccurate instances or biased/objectionable outputs.
Before using sarashina2, we would like developers to tune models based on human preferences and safety considerations.
## License
[MIT License](https://huggingface.co/sbintuitions/sarashina2-7b/blob/main/LICENSE) |