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Update readme to match code, see cl/609177092
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README.md
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README.md
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@ -308,19 +308,19 @@ The code defines two C++ structs, `ConfigGemma7B` and `ConfigGemma2B`, which are
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**ConfigGemma7B**:
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* `seq_len`: Stores the length of the sequence to be processed. It's set to 7168.
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* `vocab_size`: Stores the size of the vocabulary, which is 256128.
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* `n_layers`: Number of layers in the deep learning model. It's set to 28.
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* `dim_model`: Dimension of the model's internal representation. It's set to 3072.
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* `dim_ffw_hidden`: Dimension of the feedforward and recurrent layers' hidden representations. It's set to 16 * 3072 / 2.
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* `kSeqLen`: Stores the length of the sequence to be processed. It's set to 7168.
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* `kVocabSize`: Stores the size of the vocabulary, which is 256128.
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* `kLayers`: Number of layers in the deep learning model. It's set to 28.
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* `kModelDim`: Dimension of the model's internal representation. It's set to 3072.
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* `kFFHiddenDim`: Dimension of the feedforward and recurrent layers' hidden representations. It's set to 16 * 3072 / 2.
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**ConfigGemma2B**:
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* `seq_len`: Stores the length of the sequence to be processed. It's also set to 7168.
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* `vocab_size`: Size of the vocabulary, which is 256128.
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* `n_layers`: Number of layers in the deep learning model. It's set to 18.
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* `dim_model`: Dimension of the model's internal representation. It's set to 2048.
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* `dim_ffw_hidden`: Dimension of the feedforward and recurrent layers' hidden representations. It's set to 16 * 2048 / 2.
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* `kSeqLen`: Stores the length of the sequence to be processed. It's also set to 7168.
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* `kVocabSize`: Size of the vocabulary, which is 256128.
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* `kLayers`: Number of layers in the deep learning model. It's set to 18.
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* `kModelDim`: Dimension of the model's internal representation. It's set to 2048.
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* `kFFHiddenDim`: Dimension of the feedforward and recurrent layers' hidden representations. It's set to 16 * 2048 / 2.
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These structs are used to configure a deep learning model with specific parameters for either Gemma7B or Gemma2B architecture.
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```
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