# 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. import torch from torch import nn from ... import initialization as init from ...modeling_rope_utils import RopeParameters from ...modeling_utils import PreTrainedModel from ...utils import logging from ..deepseek_v3.configuration_deepseek_v3 import DeepseekV3Config from ..deepseek_v3.modeling_deepseek_v3 import DeepseekV3Attention from ..llama.modeling_llama import ( LlamaDecoderLayer, LlamaForCausalLM, LlamaModel, LlamaPreTrainedModel, LlamaRMSNorm, LlamaRotaryEmbedding, ) from ..qwen3.modeling_qwen3 import Qwen3MLP logger = logging.get_logger(__name__) class YoutuConfig(DeepseekV3Config): 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" base_model_tp_plan = { "layers.*.mlp.gate_proj": "colwise", "layers.*.mlp.up_proj": "colwise", "layers.*.mlp.down_proj": "rowwise", } 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, ): super().__init__( vocab_size=vocab_size, hidden_size=hidden_size, intermediate_size=intermediate_size, num_hidden_layers=num_hidden_layers, num_attention_heads=num_attention_heads, num_key_value_heads=num_key_value_heads, kv_lora_rank=kv_lora_rank, q_lora_rank=q_lora_rank, qk_rope_head_dim=qk_rope_head_dim, v_head_dim=v_head_dim, qk_nope_head_dim=qk_nope_head_dim, hidden_act=hidden_act, max_position_embeddings=max_position_embeddings, rms_norm_eps=rms_norm_eps, use_cache=use_cache, pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, rope_parameters=rope_parameters, rope_interleave=rope_interleave, attention_bias=attention_bias, attention_dropout=attention_dropout, **kwargs, ) # remove unused attribute del self.n_shared_experts del self.n_routed_experts del self.routed_scaling_factor del self.n_group del self.topk_group del self.num_experts_per_tok del self.first_k_dense_replace del self.norm_topk_prob del self.pretraining_tp del self.moe_intermediate_size # 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 def convert_rope_params_to_dict(self, ignore_keys_at_rope_validation: set | None = None, **kwargs): raise AttributeError("Not overwritten for the Youtu model!") class YoutuRMSNorm(LlamaRMSNorm): pass class YoutuRotaryEmbedding(LlamaRotaryEmbedding): pass class YoutuMLP(Qwen3MLP): pass class YoutuAttention(DeepseekV3Attention): pass class YoutuDecoderLayer(LlamaDecoderLayer): pass class YoutuPreTrainedModel(LlamaPreTrainedModel, PreTrainedModel): @torch.no_grad() def _init_weights(self, module): PreTrainedModel._init_weights(self, module) std = getattr(self.config, "initializer_range", 0.02) embed_std = getattr(self.config, "embedding_initializer_range", 2 * std) if isinstance(module, nn.Embedding): init.normal_(module.weight, mean=0.0, std=embed_std) if module.padding_idx is not None: init.zeros_(module.weight.data[module.padding_idx]) class YoutuModel(LlamaModel): pass class YoutuForCausalLM(LlamaForCausalLM): pass __all__ = [ "YoutuConfig", "YoutuPreTrainedModel", "YoutuModel", "YoutuForCausalLM", ]