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Model: sbintuitions/sarashina2-13b Source: Original Platform
2026-05-01 16:41:57 +08:00
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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)