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""" |
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NeuralQuantum NQLM Configuration for Hugging Face Transformers |
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""" |
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from transformers import PretrainedConfig |
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class NeuralQuantumNQLMConfig(PretrainedConfig): |
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"""Configuration class for NeuralQuantum NQLM model""" |
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model_type = "neuralquantum_nqlm" |
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def __init__( |
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self, |
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vocab_size=50257, |
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hidden_size=768, |
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num_attention_heads=12, |
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num_hidden_layers=12, |
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intermediate_size=3072, |
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hidden_act="gelu", |
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hidden_dropout_prob=0.1, |
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attention_probs_dropout_prob=0.1, |
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max_position_embeddings=512, |
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type_vocab_size=2, |
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initializer_range=0.02, |
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layer_norm_eps=1e-12, |
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use_cache=True, |
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quantum_enhancement=True, |
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quantum_layers=4, |
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quantum_circuit_depth=8, |
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quantum_optimization="vqe", |
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hybrid_mode=True, |
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torch_dtype="float16", |
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**kwargs |
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): |
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super().__init__(**kwargs) |
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self.vocab_size = vocab_size |
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self.hidden_size = hidden_size |
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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.intermediate_size = intermediate_size |
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self.hidden_act = hidden_act |
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self.hidden_dropout_prob = hidden_dropout_prob |
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self.attention_probs_dropout_prob = attention_probs_dropout_prob |
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self.max_position_embeddings = max_position_embeddings |
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self.type_vocab_size = type_vocab_size |
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self.initializer_range = initializer_range |
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self.layer_norm_eps = layer_norm_eps |
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self.use_cache = use_cache |
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self.quantum_enhancement = quantum_enhancement |
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self.quantum_layers = quantum_layers |
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self.quantum_circuit_depth = quantum_circuit_depth |
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self.quantum_optimization = quantum_optimization |
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self.hybrid_mode = hybrid_mode |
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self.torch_dtype = torch_dtype |
