# coding=utf-8 # Copyright (c) 2025, Qwerky AI, Inc. 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. """QwerkyLlamaMambaHybrid model configuration""" from typing import List, Optional from transformers.configuration_utils import PretrainedConfig from transformers.utils import logging logger = logging.get_logger(__name__) class QwerkyLlamaMambaHybridConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`MambaInLlamaMambaModel`]. It consolidates both the transformer config and mamba config into a single configuration file. 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 32000): Vocabulary size of the MambaInLlama model. hidden_size (`int`, *optional*, defaults to 4096): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 11008): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 32): Number of hidden layers in the model. num_attention_heads (`int`, *optional*, defaults to 32): Number of attention heads for each attention layer. num_key_value_heads (`int`, *optional*, defaults to 32): Number of key-value heads for grouped query attention. hidden_act (`str`, *optional*, defaults to "silu"): The non-linear activation function in the MLP layers. max_position_embeddings (`int`, *optional*, defaults to 2048): The maximum sequence length that this model might ever be used with. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. rms_norm_eps (`float`, *optional*, defaults to 1e-6): 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. pad_token_id (`int`, *optional*, defaults to 0): The id of the padding token. bos_token_id (`int`, *optional*, defaults to 1): The id of the "beginning-of-sequence" token. eos_token_id (`int`, *optional*, defaults to 2): The id of the "end-of-sequence" token. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether the model's input and output word embeddings should be tied. rope_theta (`float`, *optional*, defaults to 10000.0): The base period of the RoPE embeddings. rope_scaling (`dict`, *optional*): Dictionary containing the scaling configuration for the RoPE embeddings. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. # Mamba-specific config d_model (`int`, *optional*): Model dimension for Mamba layers. If not provided, defaults to `hidden_size`. d_inner (`int`, *optional*): Inner dimension for Mamba layers. If not provided, defaults to `intermediate_size`. d_xb (`int`, *optional*, defaults to 2560): Dimension for Mamba xB projection. ssm_cfg (`dict`, *optional*, defaults to `{}`): State space model configuration dictionary. attn_layers (`List[int]`, *optional*, defaults to `[]`): List of layer indices that use attention instead of Mamba. """ model_type = "qwerky_llama_mamba_hybrid" keys_to_ignore_at_inference = ["past_key_values"] def __init__( self, vocab_size: int = 32000, hidden_size: int = 4096, intermediate_size: int = 11008, num_hidden_layers: int = 32, num_attention_heads: int = 32, num_key_value_heads: Optional[int] = None, hidden_act: str = "silu", max_position_embeddings: int = 2048, initializer_range: float = 0.02, rms_norm_eps: float = 1e-6, use_cache: bool = True, pad_token_id: int = 0, bos_token_id: int = 1, eos_token_id: int = 2, tie_word_embeddings: bool = False, rope_theta: float = 10000.0, rope_scaling: Optional[dict] = None, attention_dropout: float = 0.0, # Mamba-specific parameters d_model: Optional[int] = None, d_inner: Optional[int] = None, d_xb: int = 2560, ssm_cfg: Optional[dict] = None, attn_layers: Optional[List[int]] = None, **kwargs, ): self.vocab_size = vocab_size 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.num_key_value_heads = ( num_key_value_heads if num_key_value_heads is not None else num_attention_heads ) self.hidden_act = hidden_act self.max_position_embeddings = max_position_embeddings self.initializer_range = initializer_range self.rms_norm_eps = rms_norm_eps self.use_cache = use_cache self.rope_theta = rope_theta self.rope_scaling = rope_scaling self.attention_dropout = attention_dropout # Mamba-specific parameters self.d_model = d_model if d_model is not None else hidden_size self.d_inner = d_inner if d_inner is not None else intermediate_size self.d_xb = d_xb self.ssm_cfg = ssm_cfg if ssm_cfg is not None else {} self.attn_layers = attn_layers if attn_layers is not None else [] super().__init__( pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs, ) # Set auto_map for external code loading if "auto_map" not in kwargs: self.auto_map = { "AutoConfig": "configuration_qwerky_llama_mamba_hybrid.QwerkyLlamaMambaHybridConfig", "AutoModelForCausalLM": "modeling_qwerky_llama_mamba_hybrid.QwerkyLlamaMambaHybridForCausalLM", } # Set architectures field if "architectures" not in kwargs: self.architectures = ["QwerkyLlamaMambaHybridForCausalLM"]