Add configuration_hf_nomic_bert.py
Browse files
configuration_hf_nomic_bert.py
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from transformers import GPT2Config
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class NomicBertConfig(GPT2Config):
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model_type = "nomic_bert"
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def __init__(
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self,
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prenorm=False,
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parallel_block=False,
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parallel_block_tied_norm=False,
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rotary_emb_fraction=0.0,
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fused_dropout_add_ln=False,
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fused_bias_fc=False,
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use_flash_attn=False,
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use_xentropy=False,
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qkv_proj_bias=True,
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rotary_emb_base=10_000,
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rotary_emb_scale_base=None,
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rotary_emb_interleaved=False,
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mlp_fc1_bias=True,
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mlp_fc2_bias=True,
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use_rms_norm=False,
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causal=False,
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type_vocab_size=2,
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dense_seq_output=True,
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pad_vocab_size_multiple=1,
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tie_word_embeddings=True,
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rotary_scaling_factor=None,
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max_trained_positions=2048,
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**kwargs,
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):
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self.prenorm = prenorm
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self.parallel_block = parallel_block
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self.parallel_block_tied_norm = parallel_block_tied_norm
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self.rotary_emb_fraction = rotary_emb_fraction
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self.tie_word_embeddings = tie_word_embeddings
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self.fused_dropout_add_ln = fused_dropout_add_ln
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self.fused_bias_fc = fused_bias_fc
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self.use_flash_attn = use_flash_attn
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self.use_xentropy = use_xentropy
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self.qkv_proj_bias = qkv_proj_bias
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self.rotary_emb_base = rotary_emb_base
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self.rotary_emb_scale_base = rotary_emb_scale_base
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self.rotary_emb_interleaved = rotary_emb_interleaved
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self.mlp_fc1_bias = mlp_fc1_bias
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self.mlp_fc2_bias = mlp_fc2_bias
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self.use_rms_norm = use_rms_norm
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self.causal = causal
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self.type_vocab_size = type_vocab_size
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self.dense_seq_output = dense_seq_output
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self.pad_vocab_size_multiple = pad_vocab_size_multiple
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self.rotary_scaling_factor = rotary_scaling_factor
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self.max_trained_positions = max_trained_positions
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super().__init__(**kwargs)
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