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from transformers.configuration_utils import PretrainedConfig |
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class PhariaConfig(PretrainedConfig): |
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model_type = "pharia-v1" |
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def __init__( |
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self, |
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pad_token_id=None, |
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bos_token_id=1, |
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eos_token_id=2, |
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hidden_act="gelu", |
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hidden_size=512, |
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initializer_range=0.02, |
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intermediate_size=2048, |
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max_position_embeddings=8192, |
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model_type="pharia-v1", |
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num_attention_heads=4, |
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num_hidden_layers=4, |
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num_key_value_heads=2, |
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torch_dtype="bfloat16", |
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transformers_version="4.31.0.dev0", |
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use_cache=True, |
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vocab_size=128000, |
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mlp_bias=True, |
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attention_bias=True, |
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tie_word_embeddings=False, |
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attention_dropout=0.0, |
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rope_theta=1000000, |
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rope_scaling=None, |
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**kwargs, |
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): |
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super().__init__( |
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pad_token_id=pad_token_id, |
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bos_token_id=bos_token_id, |
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eos_token_id=eos_token_id, |
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tie_word_embeddings=tie_word_embeddings, |
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**kwargs, |
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) |
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self.pad_token_id = pad_token_id |
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self.bos_token_id = bos_token_id |
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self.eos_token_id = eos_token_id |
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self.hidden_act = hidden_act |
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self.hidden_size = hidden_size |
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self.initializer_range = initializer_range |
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self.intermediate_size = intermediate_size |
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self.max_position_embeddings = max_position_embeddings |
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self.model_type = model_type |
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self.num_attention_heads = num_attention_heads |
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self.num_hidden_layers = num_hidden_layers |
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self.num_key_value_heads = num_key_value_heads |
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self.torch_dtype = torch_dtype |
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self.transformers_version = transformers_version |
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self.use_cache = use_cache |
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self.vocab_size = vocab_size |
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self.mlp_bias = mlp_bias |
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self.attention_bias = attention_bias |
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self.tie_word_embeddings = tie_word_embeddings |
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self.attention_dropout = attention_dropout |
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self.rope_theta = rope_theta |
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self.rope_scaling = rope_scaling |
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