update

vllm/config/model_arch.py

@@ -0,0 +1,57 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from typing import Any
+
+from pydantic import ConfigDict
+from pydantic.dataclasses import dataclass
+
+from vllm.logger import init_logger
+
+logger = init_logger(__name__)
+
+
+@dataclass(config=ConfigDict(arbitrary_types_allowed=True))
+class ModelArchitectureConfig:
+    """
+    Configuration for model architecture that required by vLLM runtime
+    """
+
+    architectures: list[str] | None
+    """List of model architecture class names (e.g., ['LlamaForCausalLM']).
+       It can be None upon calling `vllm_config.with_hf_config(config.text_config)`"""
+
+    model_type: str
+    """Model type identifier (e.g., 'llama', 'gpt_oss')."""
+
+    text_model_type: str | None
+    """Text model type identifier (e.g., 'llama4_text')."""
+
+    hidden_size: int
+    """Hidden size of the model."""
+
+    total_num_hidden_layers: int
+    """Number of hidden layers in the model."""
+
+    total_num_attention_heads: int
+    """Number of attention heads in the model."""
+
+    head_size: int
+    """Head dimension of the model."""
+
+    vocab_size: int
+    """Vocabulary size of the model."""
+
+    total_num_kv_heads: int
+    """Number of key value heads in the model."""
+
+    num_experts: int
+    """Number of experts in the model."""
+
+    quantization_config: dict[str, Any] | None
+    """Quantization configuration dictionary containing quantization parameters."""
+
+    is_deepseek_mla: bool
+    """Whether the model is a DeepSeek MLA model."""
+
+    derived_max_model_len_and_key: tuple[float, str | None]
+    """Derived maximum model length and key from the hf config."""