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README.md

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+
+---
+
+license: mit
+language:
+- ja
+- en
+
+---
+
+![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)
+
+# QuantFactory/sarashina2-7b-GGUF
+This is quantized version of [sbintuitions/sarashina2-7b](https://huggingface.co/sbintuitions/sarashina2-7b) created using llama.cpp
+
+# Original Model Card
+
+
+# Sarashina2-7B
+
+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/sarashina2-7b", torch_dtype=torch.bfloat16, device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained("sbintuitions/sarashina2-7b")
+# If you want to use slow tokenizer
+# tokenizer = AutoTokenizer.from_pretrained("sbintuitions/sarashina2-7b", 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)
+ 
+# These examples are generated by sarashina2-7b parameters model
+# {'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'}
+# {'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。'}
+# {'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'}
+```
+
+## 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)