""" Kimi K25 Model Configuration. """ from transformers import DeepseekV3Config from transformers.configuration_utils import PretrainedConfig class KimiK25VisionConfig(PretrainedConfig): """Vision configuration for K2-VL (vision tower + mm projector). Args: Vision Tower Parameters: patch_size: Patch size for vision tower. init_pos_emb_height: Initial position embedding height. init_pos_emb_width: Initial position embedding width. init_pos_emb_time: Initial position embedding time dimension. pos_emb_type: Type of position embedding. num_attention_heads: Number of attention heads in vision tower. num_hidden_layers: Number of hidden layers in vision tower. hidden_size: Hidden size of vision tower. intermediate_size: Intermediate size in vision tower FFN. merge_kernel_size: Kernel size for spatial patch merging. video_attn_type: Type of video attention. merge_type: Type of merge operation. MM Projector Parameters: mm_projector_type: Type of multimodal projector. mm_hidden_size: Hidden size for projector (defaults to hidden_size). projector_hidden_act: Activation function for projector. projector_ln_eps: Layer norm epsilon for projector. """ model_type = "kimi_k25" def __init__( self, # Vision Tower patch_size: int = 14, init_pos_emb_height: int = 64, init_pos_emb_width: int = 64, init_pos_emb_time: int = 4, pos_emb_type: str = "divided_fixed", num_attention_heads: int = 16, num_hidden_layers: int = 27, hidden_size: int = 1152, intermediate_size: int = 4304, merge_kernel_size: tuple[int, int] = (2, 2), video_attn_type: str = "spatial_temporal", merge_type: str = "sd2_tpool", # MM Projector mm_projector_type: str = "patchmerger", mm_hidden_size: int | None = None, projector_hidden_act: str = "gelu", projector_ln_eps: float = 1e-5, text_hidden_size: int = 7168, **kwargs, ): super().__init__(**kwargs) # Vision Tower self.patch_size = patch_size self.init_pos_emb_height = init_pos_emb_height self.init_pos_emb_width = init_pos_emb_width self.init_pos_emb_time = init_pos_emb_time self.pos_emb_type = pos_emb_type self.num_attention_heads = num_attention_heads self.num_hidden_layers = num_hidden_layers self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.merge_kernel_size = merge_kernel_size self.video_attn_type = video_attn_type self.merge_type = merge_type # MM Projector self.mm_projector_type = mm_projector_type if mm_hidden_size is not None: self.mm_hidden_size = mm_hidden_size else: self.mm_hidden_size = hidden_size self.projector_hidden_act = projector_hidden_act self.projector_ln_eps = projector_ln_eps self.text_hidden_size = text_hidden_size class KimiK25Config(PretrainedConfig): """K2-VL model configuration. K2-VL extends Kimi-VL with video support using video-chunks. A video-chunk consists of multiple consecutive frames (default: 4) that are processed together with temporal pooling. Args: text_config: Configuration for the text model (DeepseekV3). Vision Tower Parameters: patch_size: Patch size for vision tower. init_pos_emb_height: Initial position embedding height. init_pos_emb_width: Initial position embedding width. init_pos_emb_time: Initial position embedding time dimension. pos_emb_type: Type of position embedding. vt_num_attention_heads: Number of attention heads in vision tower. vt_num_hidden_layers: Number of hidden layers in vision tower. vt_hidden_size: Hidden size of vision tower. vt_intermediate_size: Intermediate size in vision tower FFN. merge_kernel_size: Kernel size for spatial patch merging. video_attn_type: Type of video attention. merge_type: Type of merge operation. Video-Chunk Parameters: temporal_merge_kernel_size: Number of frames per video chunk. Default is 4, meaning 4 frames are merged into 1 chunk. sample_fps: Video sampling frame rate. timestamp_mode: Format for chunk timestamps. MM Projector Parameters: mm_projector_type: Type of multimodal projector. mm_hidden_size: Hidden size from vision tower. projector_hidden_act: Activation function for projector. projector_ln_eps: Layer norm epsilon for projector. Other Parameters: ignore_index: The ignore index for the loss function. media_placeholder_token_id: The token ID for media placeholders. pad_token_id: The token ID for padding. """ model_type = "kimi_k25" def __init__( self, text_config: dict | DeepseekV3Config | None = None, vision_config: dict | KimiK25VisionConfig | None = None, # Other parameters ignore_index: int = -100, media_placeholder_token_id: int = 163605, pad_token_id: int = 0, use_unified_vision_chunk: bool = False, video_placeholder: str = "<|kimi_k25_video_placeholder|>", **kwargs, ): if text_config is None: text_config = DeepseekV3Config() elif isinstance(text_config, dict): text_config = DeepseekV3Config(**text_config) if vision_config is None: vision_config = KimiK25VisionConfig() elif isinstance(vision_config, dict): vision_config = KimiK25VisionConfig(**vision_config) self.vision_config = vision_config self.text_config = text_config # Other config self.ignore_index = ignore_index self.media_placeholder_token_id = media_placeholder_token_id self.use_unified_vision_chunk = use_unified_vision_chunk self.video_placeholder = video_placeholder # Propagate quantization config from text model if getattr(self.text_config, "quantization_config", None) is not None: self.quantization_config = self.text_config.quantization_config super().__init__(pad_token_id=pad_token_id, **kwargs) @property def hidden_size(self) -> int: """Get hidden size from text config for compatibility.""" return self.text_config.hidden_size @property def vocab_size(self) -> int: """Get vocab size from text config for compatibility.""" return self.text_config.vocab_size