Update README.md

README.md

@@ -1,67 +1,67 @@
----
-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
-
-```
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, set_seed
- 
-model = AutoModelForCausalLM.from_pretrained("sbintuitions/sarashina2-13b", torch_dtype=torch.bfloat16)
-tokenizer = AutoTokenizer.from_pretrained("sbintuitions/sarashina2-13b", use_fast=False)
-generator = pipeline("text-generation", model=model, tokenizer=tokenizer, device_map="auto")
-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 | Trainning 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 (TBA)| | | | | | |     
-
-## 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
-
+---
+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
+
+```
+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", 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 | Trainning 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 (TBA)| | | | | | |     
+
+## 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)