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configuration_HelpingAI.py

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+""" HelpingAI model configuration"""
+
+from transformers import PretrainedConfig
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+class HelpingAIConfig(PretrainedConfig):
+    keys_to_ignore_at_inference = ["past_key_values"]
+    model_type = "HelpingAI"
+    def __init__(
+        self,
+        vocab_size=50304,
+        hidden_size=2560,
+        intermediate_size=6912,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=32,
+        head_dim=256,
+        hidden_act="silu",
+        max_position_embeddings=4096,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        hidden_activation=None,
+        rope_theta=10000,
+        rope_pct=0.25,
+        attention_bias=False,
+        attention_dropout=0.0,
+        num_experts_per_tok=2,
+        num_local_experts=8,
+        router_aux_loss_coef=0.02,
+        output_router_logits=False,
+        norm_eps=1.0e-5,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings        
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.head_dim = head_dim
+        self.hidden_act = hidden_act
+        self.hidden_activation = hidden_activation
+        self.num_key_value_heads = num_key_value_heads
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.attention_bias = attention_bias 
+        self.attention_dropout = attention_dropout
+        self.num_experts_per_tok = num_experts_per_tok
+        self.num_local_experts = num_local_experts
+        self.router_aux_loss_coef = router_aux_loss_coef
+        self.output_router_logits = output_router_logits
+        self.rope_pct = rope_pct
+        self.norm_eps = norm_eps
+        super().__init__(**kwargs)