# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # This file was automatically generated from src/transformers/models/youtu/modular_youtu.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_youtu.py file directly. One of our CI enforces this. # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # Copyright 2026 the Tencent and HuggingFace Inc. team. All rights reserved. # # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX # and OPT implementations in this library. It has been modified from its # original forms to accommodate minor architectural differences compared # to GPT-NeoX and OPT used by the Meta AI team that trained the model. # # 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 YoutuConfig(PreTrainedConfig): r""" This is the configuration class to store the configuration of a [`YoutuModel`]. It is used to instantiate an Youtu 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 Youtu-LLM-2B. e.g. [tencent/Youtu-LLM-2B](https://huggingface.co/tencent/Youtu-LLM-2B) Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PreTrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 128256): Vocabulary size of the Deep model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`YoutuModel`] hidden_size (`int`, *optional*, defaults to 2048): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 6144): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 32): Number of hidden layers in the Transformer decoder. num_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer decoder. num_key_value_heads (`int`, *optional*, defaults to 16): In MLA, num_key_value_heads=num_attention_heads. kv_lora_rank (`int`, *optional*, defaults to 512): Rank of the LoRA matrices for key and value projections. q_lora_rank (`int`, *optional*, defaults to 1536): Rank of the LoRA matrices for query projections. qk_rope_head_dim (`int`, *optional*, defaults to 64): Dimension of the query/key heads that use rotary position embeddings. v_head_dim (`int`, *optional*, defaults to 128): Dimension of the value heads. qk_nope_head_dim (`int`, *optional*, defaults to 128): Dimension of the query/key heads that don't use rotary position embeddings. hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): The non-linear activation function (function or string) in the decoder. max_position_embeddings (`int`, *optional*, defaults to 131072): The maximum sequence length that this model might ever be used with. initializer_range (`float`, *optional*): The standard deviation of the truncated_normal_initializer for initializing all weight matrices, except embedding matrices. embedding_initializer_range (`float`, *optional*): The standard deviation of the truncated_normal_initializer for initializing all embedding matrices. rms_norm_eps (`float`, *optional*, defaults to 1e-06): The epsilon used by the rms normalization layers. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if `config.is_decoder=True`. pad_token_id (`int`, *optional*): Padding token id. bos_token_id (`int`, *optional*, defaults to 128000): Beginning of stream token id. eos_token_id (`int`, *optional*, defaults to 128001): End of stream token id. tie_word_embeddings (`bool`, *optional*, defaults to `True`): Whether to tie weight embeddings rope_parameters (`RopeParameters`, *optional*): Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE with longer `max_position_embeddings`. rope_interleave (`bool`, *optional*, defaults to `True`): Whether to interleave the rotary position embeddings. attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`): Whether to use a bias in the query, key, value and output projection layers during self-attention. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. ```python >>> from transformers import YoutuModel, YoutuConfig >>> # Initializing a Youtu-LLM-2B style configuration >>> configuration = YoutuConfig() >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "youtu" keys_to_ignore_at_inference = ["past_key_values"] base_model_tp_plan = { "layers.*.mlp.gate_proj": "colwise", "layers.*.mlp.up_proj": "colwise", "layers.*.mlp.down_proj": "rowwise", } base_model_pp_plan = { "embed_tokens": (["input_ids"], ["inputs_embeds"]), "layers": (["hidden_states", "attention_mask"], ["hidden_states"]), "norm": (["hidden_states"], ["hidden_states"]), } attribute_map = {} def __init__( self, vocab_size: int | None = 128256, hidden_size: int | None = 2048, intermediate_size: int | None = 6144, num_hidden_layers: int | None = 32, num_attention_heads: int | None = 16, num_key_value_heads: int | None = 16, kv_lora_rank: int | None = 512, q_lora_rank: int | None = 1536, qk_rope_head_dim: int | None = 64, v_head_dim: int | None = 128, qk_nope_head_dim: int | None = 128, hidden_act: str | None = "silu", max_position_embeddings: int | None = 131072, initializer_range: float | None = None, embedding_initializer_range: float | None = None, rms_norm_eps: int | None = 1e-6, use_cache: bool | None = True, pad_token_id: int | None = None, bos_token_id: int | None = 128000, eos_token_id: int | None = 128001, tie_word_embeddings: bool | None = True, rope_parameters: RopeParameters | dict[str, RopeParameters] = None, rope_interleave: bool | None = True, attention_bias: bool | None = False, attention_dropout: float | None = 0.0, **kwargs, ): self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.kv_lora_rank = kv_lora_rank self.q_lora_rank = q_lora_rank self.qk_rope_head_dim = qk_rope_head_dim self.v_head_dim = v_head_dim self.qk_nope_head_dim = qk_nope_head_dim self.qk_head_dim = qk_nope_head_dim + qk_rope_head_dim self.head_dim = qk_rope_head_dim self.rope_interleave = rope_interleave # for backward compatibility if num_key_value_heads is None: num_key_value_heads = num_attention_heads self.num_key_value_heads = num_key_value_heads self.hidden_act = hidden_act self.initializer_range = initializer_range self.rms_norm_eps = rms_norm_eps self.use_cache = use_cache self.attention_bias = attention_bias self.attention_dropout = attention_dropout self.rope_parameters = rope_parameters self.tie_word_embeddings = tie_word_embeddings self.pad_token_id = pad_token_id self.bos_token_id = bos_token_id self.eos_token_id = eos_token_id super().__init__(**kwargs) # if initializer_range is None, set it to 2.0 / (5.0 * self.hidden_size) ** 0.5 (if hidden size is valid) if self.initializer_range is None: if self.hidden_size != 0: self.initializer_range = 2.0 / (5.0 * self.hidden_size) ** 0.5 else: self.initializer_range = 0.02 # if embedding_initializer_range is None, set it to 2.0 * self.initializer_range if embedding_initializer_range is None: self.embedding_initializer_range = 2.0 * self.initializer_range else: self.embedding_initializer_range = embedding_initializer_range __all__ = ["YoutuConfig"]