codingutf8 2022 abeja inc and the huggingface inc team licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache orglicenseslicense2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license gptneox japanese model configuration from configurationutils import pretrainedconfig from utils import logging logger logging getloggername gptneoxjapanesepretrainedconfigarchivemap abejagptneoxjapanese2 7b https huggingface coabejagptneoxjapanese2 7bresolvemainconfig json class gptneoxjapaneseconfigpretrainedconfig r this is the configuration class to store the configuration of a gptneoxmodeljapanese it is used to instantiate a gptneox model according to the specified arguments defining the model architecture instantiating a configuration with the defaults will yield a similar configuration to that of the gptneoxjapanese abejagptneoxjapanese2 7bhttps huggingface coabejagptneoxjapanese2 7b architecture configuration objects inherit from pretrainedconfig and can be used to control the model outputs read the documentation from pretrainedconfig for more information default configs is set as 2 7b model args vocabsize int optional defaults to 32000 vocabulary size of the gptneoxjapanese model defines the number of different tokens that can be represented by the inputsids passed when calling gptneoxjapanese hiddensize int optional defaults to 2560 dimension of the encoder layers and the pooler layer numhiddenlayers int optional defaults to 32 number of hidden layers in the transformer encoder numattentionheads int optional defaults to 32 number of attention heads for each attention layer in the transformer encoder intermediatemultiplesize int optional defaults to 4 dimension of the intermediate layer in the transformer encoder is calculated by hiddensize intermediatemultiplesize hiddenact str or function optional defaults to gelu the nonlinear activation function function or string in the encoder and pooler rotarypct float optional defaults to 1 00 percentage of hidden dimensions to allocate to rotary embeddings rotaryembbase int optional defaults to 10000 base for computing rotary embeddings frequency maxpositionembeddings int optional defaults to 2048 the maximum sequence length that this model might ever be used with initializerrange float optional defaults to 0 02 the standard deviation of the truncatednormalinitializer for initializing all weight matrices layernormeps float optional defaults to 1e5 the epsilon used by the layer normalization layers usecache bool optional defaults to true whether or not the model should return the last keyvalues attentions not used by all models only relevant if config isdecodertrue attentiondropout float optional defaults to 0 1 the dropout ratio for the attention hiddendropout float optional defaults to 0 0 the dropout ratio for the hidden layer example python from transformers import gptneoxjapaneseconfig gptneoxjapanesemodel initializing a gptneoxjapanese gptneoxjapanese2 7b style configuration configuration gptneoxjapaneseconfig initializing a model with random weights from the gptneoxjapanese2 7b style configuration model gptneoxjapanesemodelconfiguration accessing the model configuration configuration model config modeltype gptneoxjapanese def init self vocabsize32000 hiddensize2560 numhiddenlayers32 numattentionheads32 intermediatemultiplesize4 hiddenactgelu rotarypct1 00 rotaryembbase10000 maxpositionembeddings2048 initializerrange0 02 layernormeps1e5 usecachetrue bostokenid31996 eostokenid31999 attentiondropout0 1 hiddendropout0 0 kwargs super initbostokenidbostokenid eostokenideostokenid kwargs self vocabsize vocabsize self maxpositionembeddings maxpositionembeddings self hiddensize hiddensize self numhiddenlayers numhiddenlayers self numattentionheads numattentionheads self intermediatemultiplesize intermediatemultiplesize self hiddenact hiddenact self rotarypct rotarypct self rotaryembbase rotaryembbase self initializerrange initializerrange self layernormeps layernormeps self usecache usecache self attentiondropout attentiondropout self hiddendropout hiddendropout coding utf 8 2022 abeja inc and the huggingface inc team licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license gptneox japanese model configuration this is the configuration class to store the configuration of a gptneoxmodeljapanese it is used to instantiate a gptneox model according to the specified arguments defining the model architecture instantiating a configuration with the defaults will yield a similar configuration to that of the gptneoxjapanese abeja gpt neox japanese 2 7b https huggingface co abeja gpt neox japanese 2 7b architecture configuration objects inherit from pretrainedconfig and can be used to control the model outputs read the documentation from pretrainedconfig for more information default configs is set as 2 7b model args vocab_size int optional defaults to 32000 vocabulary size of the gptneoxjapanese model defines the number of different tokens that can be represented by the inputs_ids passed when calling gptneoxjapanese hidden_size int optional defaults to 2560 dimension of the encoder layers and the pooler layer num_hidden_layers int optional defaults to 32 number of hidden layers in the transformer encoder num_attention_heads int optional defaults to 32 number of attention heads for each attention layer in the transformer encoder intermediate_multiple_size int optional defaults to 4 dimension of the intermediate layer in the transformer encoder is calculated by hidden_size intermediate_multiple_size hidden_act str or function optional defaults to gelu the non linear activation function function or string in the encoder and pooler rotary_pct float optional defaults to 1 00 percentage of hidden dimensions to allocate to rotary embeddings rotary_emb_base int optional defaults to 10000 base for computing rotary embeddings frequency max_position_embeddings int optional defaults to 2048 the maximum sequence length that this model might ever be used with initializer_range float optional defaults to 0 02 the standard deviation of the truncated_normal_initializer for initializing all weight matrices layer_norm_eps float optional defaults to 1e 5 the epsilon used by the layer normalization layers use_cache bool optional defaults to true whether or not the model should return the last key values attentions not used by all models only relevant if config is_decoder true attention_dropout float optional defaults to 0 1 the dropout ratio for the attention hidden_dropout float optional defaults to 0 0 the dropout ratio for the hidden layer example python from transformers import gptneoxjapaneseconfig gptneoxjapanesemodel initializing a gptneoxjapanese gpt neox japanese 2 7b style configuration configuration gptneoxjapaneseconfig initializing a model with random weights from the gpt neox japanese 2 7b style configuration model gptneoxjapanesemodel configuration accessing the model configuration configuration model config
from ...configuration_utils import PretrainedConfig
from ...utils import logging


logger = logging.get_logger(__name__)

GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "abeja/gpt-neox-japanese-2.7b": "https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json",
}


class GPTNeoXJapaneseConfig(PretrainedConfig):
    r

    model_type = "gpt_neox_japanese"

    def __init__(
        self,
        vocab_size=32000,
        hidden_size=2560,
        num_hidden_layers=32,
        num_attention_heads=32,
        intermediate_multiple_size=4,
        hidden_act="gelu",
        rotary_pct=1.00,
        rotary_emb_base=10000,
        max_position_embeddings=2048,
        initializer_range=0.02,
        layer_norm_eps=1e-5,
        use_cache=True,
        bos_token_id=31996,
        eos_token_id=31999,
        attention_dropout=0.1,
        hidden_dropout=0.0,
        **kwargs,
    ):
        super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
        self.vocab_size = vocab_size
        self.max_position_embeddings = max_position_embeddings
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.intermediate_multiple_size = intermediate_multiple_size
        self.hidden_act = hidden_act
        self.rotary_pct = rotary_pct
        self.rotary_emb_base = rotary_emb_base
        self.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps
        self.use_cache = use_cache
        self.attention_dropout = attention_dropout
        self.hidden_dropout = hidden_dropout