Update readme to match code, see cl/609177092

PiperOrigin-RevId: 609912278
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Jan Wassenberg 2024-02-23 20:42:56 -08:00 committed by Dan Zheng
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1 changed files with 10 additions and 10 deletions

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