license: mit
language:
- ja
Sarashina1-13B
This repository provides Japanese language models trained by SB Intuitions.
How to use
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, set_seed
model = AutoModelForCausalLM.from_pretrained("sbintuitions/sarashina1-13b", torch_dtype=torch.float16, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("sbintuitions/sarashina1-13b")
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 | 51200 | 1.0T | GPTNeoX | RoPE | 32 | 4096 | 32 |
| 13B | 51200 | 1.0T | GPTNeoX | RoPE | 40 | 5120 | 40 |
| 65B | 51200 | 800B | GPTNeoX | RoPE | 80 | 8192 | 64 |
Training Corpus
We used a Japanese portion of the Common Crawl corpus, which is the largest Web corpus, as our training dataset. To clean the training corpus, we used CCNet and HojiChar. After cleaning, our corpus contains about 550B tokens.
Tokenization
We use a 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.