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README.md
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license: mit
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language:
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- ja
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---
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# Sarashina1-7B
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This repository provides Japanese language models trained by [SB Intuitions](https://www.sbintuitions.co.jp/).
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## How to use
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```
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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/sarashina1-7b", torch_dtype=torch.float16)
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tokenizer = AutoTokenizer.from_pretrained("sbintuitions/sarashina1-7b", 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 sarashina1-7b 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!!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'}
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# {'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'}
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# {'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'}
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```
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## Configuration
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| Parameters | Vocab size | Trainning tokens | Architecture | Position type | Layers | Hidden dim | Attention heads |
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| :-----: | :-----------: | :-------------: | :----------- | :-----------: | :----: | :--------: | :-------------: |
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| [7B](https://huggingface.co/sbintuitions/sarashina1-7b) | 51200 | 1.0T | GPTNeoX | RoPE | 32 | 4096 | 32 |
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| [13B](https://huggingface.co/sbintuitions/sarashina1-13b) | 51200 | 1.0T | GPTNeoX | RoPE | 40 | 5120 | 40 |
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| [65B](https://huggingface.co/sbintuitions/sarashina1-65b) | 51200 | 800B | GPTNeoX | RoPE | 80 | 8192 | 64 |
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## Training Corpus
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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 corpus contains about 550B tokens.
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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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Sarashina1 has not been tuned to follow an instruction yet.
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Therefore, sarashina1 might generate some meaningless sequences, some inaccurate instances or biased/objectionable outputs.
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Before using sarashina1, 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/sarashina1-7b/blob/main/LICENSE)
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---
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license: mit
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language:
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- ja
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---
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# Sarashina1-7B
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+
This repository provides Japanese language models trained by [SB Intuitions](https://www.sbintuitions.co.jp/).
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+
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+
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## How to use
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```
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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/sarashina1-7b", torch_dtype=torch.float16, device_map="auto")
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tokenizer = AutoTokenizer.from_pretrained("sbintuitions/sarashina1-7b", 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 sarashina1-7b 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!!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'}
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# {'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'}
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# {'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'}
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```
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## Configuration
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+
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| Parameters | Vocab size | Trainning tokens | Architecture | Position type | Layers | Hidden dim | Attention heads |
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43 |
+
| :-----: | :-----------: | :-------------: | :----------- | :-----------: | :----: | :--------: | :-------------: |
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44 |
+
| [7B](https://huggingface.co/sbintuitions/sarashina1-7b) | 51200 | 1.0T | GPTNeoX | RoPE | 32 | 4096 | 32 |
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| [13B](https://huggingface.co/sbintuitions/sarashina1-13b) | 51200 | 1.0T | GPTNeoX | RoPE | 40 | 5120 | 40 |
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| [65B](https://huggingface.co/sbintuitions/sarashina1-65b) | 51200 | 800B | GPTNeoX | RoPE | 80 | 8192 | 64 |
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## Training Corpus
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+
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+
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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51 |
+
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 corpus contains about 550B tokens.
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+
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## Tokenization
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+
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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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57 |
+
We do not apply pre-tokenization with Japanese tokenizer.
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58 |
+
Thus, a user may directly feed raw sentences into the tokenizer.
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+
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+
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+
## Ethical Considerations and Limitations
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+
Sarashina1 has not been tuned to follow an instruction yet.
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63 |
+
Therefore, sarashina1 might generate some meaningless sequences, some inaccurate instances or biased/objectionable outputs.
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64 |
+
Before using sarashina1, we would like developers to tune models based on human preferences and safety considerations.
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65 |
+
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## License
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[MIT License](https://huggingface.co/sbintuitions/sarashina1-7b/blob/main/LICENSE)
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