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--- |
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language: |
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- ja |
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tags: |
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- japanese |
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- text-generation |
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- gptj |
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- pytorch |
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- transformers |
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license: apache-2.0 |
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--- |
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A 6.8 billion parameter pre-trained model for Japanese language, based on EleutherAI's Mesh Transformer JAX, that has a similar model structure to their GPT-J-6B pre-trained model. |
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EleutherAIによるMesh Transformer JAXをコードベースとした、GPT-J-6Bに似たストラクチャと約68.7億パラメータを持つ日本語pre-trainedモデルです。 |
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- We used T5Tokenizer and SentencePiece instead of GPT-2/3 tokenizer. Normalization done by SentencePiece is must for Japanese tokenizing as there are so much many more variations for common symbols than Western languages. |
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- Tokenizer has a vocabulary of 52,500 tokens and trained on Japanese Wikipedia dump as of 01 Aug 2021. |
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- The model fits within 16GB VRAM GPUs like P100 for inference up to 1688 context length. Full 2048 context length output requires 20GB VRAM or more (e.g. GTX3090/A5000). |
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- The model was trained with TPUv3-128 generously provided by Google TRC for about 4 weeks. |
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## Specifications |
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| Hyperparameter | Value | |
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|-------------------|--------| |
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| n_parameters | 6,876,450,080 | |
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| n_layers | 32 | |
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| d_model | 4,096 | |
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| d_ff | 16,384 | |
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| n_heads | 16 | |
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| d_head | 256 | |
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| n_ctx | 2,048 | |
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| n_vocab | 52,512 | |
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| position encoding | [Rotary position encodings (RoPE)](https://arxiv.org/abs/2104.09864) | |
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| RoPE dimensions | 64 | |
