# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # This file was automatically generated from src/transformers/models/timesfm2_5/modular_timesfm2_5.py. # Do NOT edit this file manually as any edits will be overwritten by the generation of # the file from the modular. If any change should be done, please apply the change to the # modular_timesfm2_5.py file directly. One of our CI enforces this. # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # Copyright 2026 the HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from ...configuration_utils import PreTrainedConfig from ...modeling_rope_utils import RopeParameters class TimesFm2_5Config(PreTrainedConfig): r""" This is the configuration class to store the configuration of a [`TimesFm2_5ModelForPrediction`]. It is used to instantiate a TimesFM 2.5 model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the TimesFM 2.5 [google/timesfm-2.5-200m-transformers](https://huggingface.co/google/timesfm-2.5-200m-transformers) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: patch_length (`int`, *optional*, defaults to 32): The length of one patch in the input sequence. context_length (`int`, *optional*, defaults to 16384): The length of the input context. horizon_length (`int`, *optional*, defaults to 128): The length of the prediction horizon. quantiles (`list[float]`, *optional*, defaults to `[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]`): The quantiles to predict. hidden_size (`int`, *optional*, defaults to 1280): Size of the hidden layers. intermediate_size (`int`, *optional*, defaults to 1280): Dimension of the MLP representations. head_dim (`int`, *optional*, defaults to 80): Size of the key, query, value projections per attention head. num_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer. num_key_value_heads (`int`, *optional*, defaults to 16): Number of key-value heads. Set equal to `num_attention_heads` for full (non-grouped) attention. num_hidden_layers (`int`, *optional*, defaults to 20): Number of Transformer layers. rms_norm_eps (`float`, *optional*, defaults to 1e-06): The epsilon used by the RMS normalization layers. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout probability for the attention scores. attention_bias (`bool`, *optional*, defaults to `False`): Whether to use bias in the attention linear projections. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. output_quantile_len (`int`, *optional*, defaults to 1024): Length of the quantile output projection dimension. decode_index (`int`, *optional*, defaults to 5): Index into the quantile dimension used to extract the point (median) forecast. use_bias (`bool`, *optional*, defaults to `False`): Whether to use bias in MLP and transformer linear layers. activation (`str`, *optional*, defaults to `"swish"`): Activation function used in MLP and residual block layers (any key from `ACT2FN`). use_continuous_quantile_head (`bool`, *optional*, defaults to `True`): Whether to use the continuous quantile head for non-median quantile predictions. force_flip_invariance (`bool`, *optional*, defaults to `True`): Whether to apply flip-invariance averaging during forecasting. infer_is_positive (`bool`, *optional*, defaults to `True`): Whether to clamp forecasts to non-negative values when the input minimum is non-negative. max_position_embeddings (`int`, *optional*, defaults to 16384): Maximum sequence length supported by the rotary position encoding. rope_parameters (`RopeParameters` or `dict[str, RopeParameters]`, *optional*): Dictionary containing the RoPE configuration. Uses default rope type with theta=10000.0 when not set. Example: ```python >>> from transformers import TimesFm2_5Config, TimesFm2_5ModelForPrediction >>> configuration = TimesFm2_5Config() >>> model = TimesFm2_5ModelForPrediction(configuration) >>> configuration = model.config ``` """ model_type = "timesfm2_5" keys_to_ignore_at_inference = [] is_encoder_decoder = False def __init__( self, patch_length: int = 32, context_length: int = 16384, horizon_length: int = 128, quantiles: list = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9], hidden_size: int = 1280, intermediate_size: int = 1280, head_dim: int = 80, num_attention_heads: int = 16, num_key_value_heads: int = 16, num_hidden_layers: int = 20, rms_norm_eps: float = 1e-6, attention_dropout: float = 0.0, attention_bias: bool = False, initializer_range: float = 0.02, output_quantile_len: int = 1024, decode_index: int = 5, use_bias: bool = False, activation: str = "swish", use_continuous_quantile_head: bool = True, force_flip_invariance: bool = True, infer_is_positive: bool = True, max_position_embeddings: int = 16384, rope_parameters: RopeParameters | dict[str, RopeParameters] | None = None, **kwargs, ): self.num_key_value_heads = num_key_value_heads self.attention_bias = attention_bias self.output_quantile_len = output_quantile_len self.decode_index = decode_index self.use_bias = use_bias self.activation = activation self.use_continuous_quantile_head = use_continuous_quantile_head self.force_flip_invariance = force_flip_invariance self.infer_is_positive = infer_is_positive self.max_position_embeddings = max_position_embeddings self.rope_parameters = rope_parameters self.patch_length = patch_length self.context_length = context_length self.horizon_length = horizon_length self.quantiles = quantiles self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.head_dim = head_dim self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.rms_norm_eps = rms_norm_eps self.attention_dropout = attention_dropout self.initializer_range = initializer_range kwargs["is_encoder_decoder"] = self.is_encoder_decoder super().__init__(**kwargs) __all__ = ["TimesFm2_5Config"]