Delete configuration_HelpingAI.py

configuration_HelpingAI.py

@@ -1,95 +0,0 @@
-""" HelpingAI model configuration"""
-
-from transformers import PretrainedConfig
-from transformers.utils import logging
-
-
-logger = logging.get_logger(__name__)
-
-
-class HelpingAIConfig(PretrainedConfig):
-    model_type = "HelpingAI"
-    keys_to_ignore_at_inference = ["past_key_values"]
-
-    def __init__(
-        self,
-        vocab_size=50281,
-        hidden_size=2560,
-        num_hidden_layers=32,
-        num_attention_heads=32,
-        head_dim=256,
-        num_local_experts=8,
-        num_experts_per_tok=2,
-        intermediate_size=6912,
-        hidden_act="silu",
-        hidden_dropout=0.0,
-        attention_dropout=0.0,
-        classifier_dropout=0.1,
-        max_position_embeddings=4096,
-        initializer_range=0.02,
-        rms_norm_eps=1e-6,
-        layer_norm_eps=1e-5,
-        use_cache=False,
-        bos_token_id=50278,
-        eos_token_id=50279,
-        pad_token_id=50279,
-        tie_word_embeddings=False,
-        rope_pct=0.25,
-        rope_theta=10000,
-        partial_rotary_factor=0.25,
-        use_qkv_bias=False,
-        output_router_logits=False,
-        router_aux_loss_coef=0.02,
-        **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.head_dim = head_dim
-        self.num_local_experts = num_local_experts
-        self.num_experts_per_tok = num_experts_per_tok
-        self.intermediate_size = intermediate_size
-        self.hidden_act = hidden_act
-        self.hidden_dropout = hidden_dropout
-        self.attention_dropout = attention_dropout
-        self.classifier_dropout = classifier_dropout
-        self.initializer_range = initializer_range
-        self.rms_norm_eps = rms_norm_eps
-        self.layer_norm_eps = layer_norm_eps
-        self.use_cache = use_cache
-        self.tie_word_embeddings = tie_word_embeddings
-        self.rope_pct = rope_pct
-        self.rope_theta = rope_theta
-        self.partial_rotary_factor = partial_rotary_factor
-        self.use_qkv_bias = use_qkv_bias
-        self.output_router_logits = output_router_logits
-        self.router_aux_loss_coef = router_aux_loss_coef
-
-        if self.hidden_size % self.num_attention_heads != 0:
-            raise ValueError(
-                "The hidden size is not divisble by the number of attention heads! Make sure to update them!"
-            )
-
-    # Copied from transformers.models.llama.configuration_llama.LlamaConfig._rope_scaling_validation
-    def _rope_scaling_validation(self):
-        """
-        Validate the `rope_scaling` configuration.
-        """
-        if self.rope_scaling is None:
-            return
-
-        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
-            raise ValueError(
-                "`rope_scaling` must be a dictionary with two fields, `type` and `factor`, " f"got {self.rope_scaling}"
-            )
-        rope_scaling_type = self.rope_scaling.get("type", None)
-        rope_scaling_factor = self.rope_scaling.get("factor", None)
-        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
-            raise ValueError(
-                f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
-            )
-        if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
-            raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")