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README.md

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 ### Training process
 We follow the approach described in [Bilingual Adaptation of Monolingual Foundation Models](https://arxiv.org/abs/2407.12869) for training.
 
-    - Starting with the Llama-3-8B base checkpoint, we extend the LLaMA-3 vocabulary by 10%, from 128,000 to 141,056 tokens, to increase a variety of Japanese Kanjis tokens. This improves Japanese tokenization efficiency by 21%.
-    - We initialize newly added embeddings using similarity-based token embedding initialization. Added embedding vectors are initialized with a weighted average of embeddings of top K most similar tokens in the original LLaMA-3 vocabulary, using an external embedding.
-    - We start with embedding-only training on 8.6B tokens, freezing the weights of all layers expect for the embedding and unembedding layers.
-    - This is followed by full continuous pre-training on 164B tokens, where all model weights are updated.
+  - Starting with the Llama-3-8B base checkpoint, we extend the LLaMA-3 vocabulary by 10%, from 128,000 to 141,056 tokens, to increase a variety of Japanese Kanjis tokens. This improves Japanese tokenization efficiency by 21%.
+  - We initialize newly added embeddings using similarity-based token embedding initialization. Added embedding vectors are initialized with a weighted average of embeddings of top K most similar tokens in the original LLaMA-3 vocabulary, using an external embedding.
+  - We start with embedding-only training on 8.6B tokens, freezing the weights of all layers expect for the embedding and unembedding layers.
+  - This is followed by full continuous pre-training on 164B tokens, where all model weights are updated.
 
 ### Training data
 This model was continuously trained on 173B tokens, with the training data consisting of 20% English and 80% Japanese. The raw Japanese data was filtered using scripts from [llm-jp-corpus repository](https://github.com/llm-jp/llm-jp-corpus). The following Japanese datasets were included into the training data mixture: