初始化项目，由ModelHub XC社区提供模型

Model: KBlueLeaf/TIPOv2-1B-A200M
Source: Original Platform

hf/configuration_kohaku.py

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+"""HF config for the Kohaku decoder. Ships inside an exported repository.
+
+Standalone by construction: an exported repo is loaded with
+``trust_remote_code=True`` on machines that do not have kohakuwullm installed, so
+nothing here may import it. See docs/guides/hf-export.md.
+"""
+
+from transformers.configuration_utils import PretrainedConfig
+
+
+class KohakuConfig(PretrainedConfig):
+    """Kohaku: GQA + per-head QK-norm, SwiGLU, and DeepSeek-style sparse MLPs.
+
+    Layers below ``first_k_dense`` use a dense SwiGLU; the rest use one shared
+    expert plus ``num_experts_per_tok`` of ``n_routed_experts``, selected on
+    sigmoid scores offset by a selection-only bias and weighted by the unbiased
+    score.
+    """
+
+    model_type = "kohaku"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size: int = 65536,
+        hidden_size: int = 768,
+        num_hidden_layers: int = 16,
+        num_attention_heads: int = 12,
+        num_key_value_heads: int = 2,
+        head_dim: int = 64,
+        intermediate_size: int = 2048,
+        moe_intermediate_size: int = 384,
+        n_routed_experts: int = 64,
+        n_shared_experts: int = 1,
+        num_experts_per_tok: int = 8,
+        first_k_dense: int = 1,
+        norm_topk_prob: bool = True,
+        routed_scaling_factor: float = 1.0,
+        scoring_func: str = "sigmoid",
+        max_position_embeddings: int = 4096,
+        rope_theta: float = 100000.0,
+        rms_norm_eps: float = 1e-6,
+        qk_norm: bool = True,
+        tie_word_embeddings: bool = False,
+        bos_token_id: int | None = 64000,
+        eos_token_id: int | None = 64001,
+        pad_token_id: int | None = 64002,
+        **kwargs,
+    ) -> None:
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads
+        self.head_dim = head_dim
+        self.intermediate_size = intermediate_size
+        self.moe_intermediate_size = moe_intermediate_size
+        self.n_routed_experts = n_routed_experts
+        self.n_shared_experts = n_shared_experts
+        self.num_experts_per_tok = num_experts_per_tok
+        self.first_k_dense = first_k_dense
+        self.norm_topk_prob = norm_topk_prob
+        self.routed_scaling_factor = routed_scaling_factor
+        self.scoring_func = scoring_func
+        self.max_position_embeddings = max_position_embeddings
+        self.rope_theta = rope_theta
+        self.rms_norm_eps = rms_norm_eps
+        self.qk_norm = qk_norm
+        super().__init__(
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            pad_token_id=pad_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )