74 lines
3.7 KiB
Markdown
74 lines
3.7 KiB
Markdown
---
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license: mit
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language:
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- ja
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- en
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---
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# Sarashina2-7B
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This repository provides large language models trained by [SB Intuitions](https://www.sbintuitions.co.jp/).
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## How to use
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, set_seed
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model = AutoModelForCausalLM.from_pretrained("sbintuitions/sarashina2-7b", torch_dtype=torch.bfloat16, device_map="auto")
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tokenizer = AutoTokenizer.from_pretrained("sbintuitions/sarashina2-7b")
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# If you want to use slow tokenizer
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# tokenizer = AutoTokenizer.from_pretrained("sbintuitions/sarashina2-7b", use_fast=False）
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generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
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set_seed(123)
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text = generator(
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"おはようございます、今日の天気は",
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max_length=30,
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do_sample=True,
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pad_token_id=tokenizer.pad_token_id,
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num_return_sequences=3,
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)
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for t in text:
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print(t)
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# These examples are generated by sarashina2-7b parameters model
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# {'generated_text': 'おはようございます、今日の天気は晴れです。ちょっと風が強い。\n昨日は、久しぶりにゆっくりとしていました。\n2週間位間があいてしまったかも、でもその間に'}
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# {'generated_text': 'おはようございます、今日の天気は曇。朝は曇っていてどんよりしていましたね。昼からは晴れそうですが。気温は徐々に上昇しています。昨日は春らしい陽気でした。'}
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# {'generated_text': 'おはようございます、今日の天気はくもり、少し寒気がします。 この土日に、家族で一泊二日で旅行に行ってきました。といっても、100キロ'}
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```
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## Configuration
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| Parameters | Vocab size | Training tokens | Architecture | Position type | Layers | Hidden dim | Attention heads |
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| :-----: | :-----------: | :-------------: | :------------ | :-----------: | :----: | :--------: | :-------------: |
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| [7B](https://huggingface.co/sbintuitions/sarashina2-7b) | 102400 | 2.1T | Llama2 | RoPE | 32 | 4096 | 32 |
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| [13B](https://huggingface.co/sbintuitions/sarashina2-13b) | 102400 | 2.1T | Llama2 | RoPE | 40 | 5120 | 40 |
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| [70B](https://huggingface.co/sbintuitions/sarashina2-70b) | 102400 | 2.1T | Llama2 | RoPE | 80 | 8192 | 64 |
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## Training Corpus
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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.
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To clean the training corpus, we used [CCNet](https://github.com/facebookresearch/cc_net) and [HojiChar](https://github.com/HojiChar/HojiChar).
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After cleaning, our Japanese training data contains about 1T tokens.
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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.
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## Tokenization
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We use a [sentencepiece](https://github.com/google/sentencepiece) tokenizer with a unigram language model and byte-fallback.
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We do not apply pre-tokenization with Japanese tokenizer.
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Thus, a user may directly feed raw sentences into the tokenizer.
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## Ethical Considerations and Limitations
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Sarashina2 has not been tuned to follow an instruction yet.
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Therefore, sarashina2 might generate some meaningless sequences, some inaccurate instances or biased/objectionable outputs.
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Before using sarashina2, we would like developers to tune models based on human preferences and safety considerations.
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## License
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[MIT License](https://huggingface.co/sbintuitions/sarashina2-7b/blob/main/LICENSE) |