Update configuration_helpingai.py

configuration_helpingai.py

@@ -1,269 +1,302 @@
-"""HelpingAI model configuration"""
-
-from transformers.configuration_utils import PretrainedConfig, layer_type_validation
-from transformers.modeling_rope_utils import rope_config_validation
-from transformers.utils import logging
-
-
-logger = logging.get_logger(__name__)
-
-
-class HelpingAIConfig(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of a [`HelpingAIModel`]. It is used to instantiate a
-    HelpingAI model according to the specified arguments, defining the model architecture. Instantiating a configuration
-    with the defaults will yield a similar configuration to that of
-    HelpingAI-8B [HelpingAI/HelpingAI-8B](https://huggingface.co/HelpingAI/HelpingAI-8B).
-
-    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 151936):
-            Vocabulary size of the HelpingAI model. Defines the number of different tokens that can be represented by the
-            `inputs_ids` passed when calling [`HelpingAIModel`]
-        hidden_size (`int`, *optional*, defaults to 4096):
-            Dimension of the hidden representations.
-        intermediate_size (`int`, *optional*, defaults to 22016):
-            Dimension of the MLP representations.
-        num_hidden_layers (`int`, *optional*, defaults to 32):
-            Number of hidden layers in the Transformer encoder.
-        num_attention_heads (`int`, *optional*, defaults to 32):
-            Number of attention heads for each attention layer in the Transformer encoder.
-        num_key_value_heads (`int`, *optional*, defaults to 32):
-            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
-            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
-            `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
-            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
-            by meanpooling all the original heads within that group. For more details, check out [this
-            paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `32`.
-        head_dim (`int`, *optional*, defaults to 128):
-            The attention head dimension.
-        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 32768):
-            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-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`.
-        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. NOTE: if you apply new rope type
-            and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
-            accordingly.
-            Expected contents:
-                `rope_type` (`str`):
-                    The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
-                    'llama3'], with 'default' being the original RoPE implementation.
-                `factor` (`float`, *optional*):
-                    Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
-                    most scaling types, a `factor` of x will enable the model to handle sequences of length x *
-                    original maximum pre-trained length.
-                `original_max_position_embeddings` (`int`, *optional*):
-                    Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
-                    pretraining.
-                `attention_factor` (`float`, *optional*):
-                    Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
-                    computation. If unspecified, it defaults to value recommended by the implementation, using the
-                    `factor` field to infer the suggested value.
-                `beta_fast` (`float`, *optional*):
-                    Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
-                    ramp function. If unspecified, it defaults to 32.
-                `beta_slow` (`float`, *optional*):
-                    Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
-                    ramp function. If unspecified, it defaults to 1.
-                `short_factor` (`list[float]`, *optional*):
-                    Only used with 'longrope'. The scaling factor to be applied to short contexts (<
-                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
-                    size divided by the number of attention heads divided by 2
-                `long_factor` (`list[float]`, *optional*):
-                    Only used with 'longrope'. The scaling factor to be applied to long contexts (<
-                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
-                    size divided by the number of attention heads divided by 2
-                `low_freq_factor` (`float`, *optional*):
-                    Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
-                `high_freq_factor` (`float`, *optional*):
-                    Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
-        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.
-        use_sliding_window (`bool`, *optional*, defaults to `False`):
-            Whether to use sliding window attention.
-        sliding_window (`int`, *optional*, defaults to 4096):
-            Sliding window attention (SWA) window size. If not specified, will default to `4096`.
-        max_window_layers (`int`, *optional*, defaults to 28):
-            The number of layers using full attention. The first `max_window_layers` layers will use full attention, while any
-            additional layer afterwards will use SWA (Sliding Window Attention).
-        layer_types (`list`, *optional*):
-            Attention pattern for each layer.
-        attention_dropout (`float`, *optional*, defaults to 0.0):
-            The dropout ratio for the attention probabilities.
-        use_emotional_reasoning (`bool`, *optional*, defaults to `True`):
-            Whether to enable Semantic Emotion Reasoning (SER) capabilities for emotional understanding and processing.
-        use_perspective_threading (`bool`, *optional*, defaults to `True`):
-            Whether to enable Perspective Emotion Threading (PET) for multi-threaded emotional reasoning.
-        num_emotion_heads (`int`, *optional*, defaults to 4):
-            Number of specialized attention heads dedicated to emotional processing and reasoning.
-        num_thinking_stages (`int`, *optional*, defaults to 3):
-            Number of thinking stages for multi-stage reasoning and reflection processing.
-        emotion_hidden_size (`int`, *optional*, defaults to 512):
-            Hidden size for the emotional reasoning layers and SER processing modules.
-        perspective_threads (`int`, *optional*, defaults to 4):
-            Number of parallel perspective threads for PET processing (relatable, supportive, motivational, analytical).
-        thinking_depth (`int`, *optional*, defaults to 2):
-            Depth of thinking layers for internal reasoning and reflection processes.
-        structured_output_vocab_size (`int`, *optional*, defaults to 100):
-            Additional vocabulary size for structured output tokens like <think>, <ser>, <pet>, etc.
-        empathy_scaling_factor (`float`, *optional*, defaults to 1.2):
-            Scaling factor for empathy-related attention weights and emotional processing.
-        reasoning_temperature (`float`, *optional*, defaults to 0.8):
-            Temperature parameter for reasoning and thinking processes to balance creativity and coherence.
-
-    ```python
-    >>> from transformers import HelpingAIModel, HelpingAIConfig
-
-    >>> # Initializing a HelpingAI style configuration with advanced reasoning
-    >>> configuration = HelpingAIConfig(
-    ...     use_emotional_reasoning=True,
-    ...     use_perspective_threading=True,
-    ...     num_emotion_heads=4,
-    ...     num_thinking_stages=3
-    ... )
-
-    >>> # Initializing a model from the HelpingAI-8B style configuration
-    >>> model = HelpingAIModel(configuration)
-
-    >>> # Accessing the model configuration
-    >>> configuration = model.config
-    ```"""
-
-    model_type = "helpingai"
-    keys_to_ignore_at_inference = ["past_key_values"]
-
-    # Default tensor parallel plan for base model `HelpingAI`
-    base_model_tp_plan = {
-        "layers.*.self_attn.q_proj": "colwise",
-        "layers.*.self_attn.k_proj": "colwise",
-        "layers.*.self_attn.v_proj": "colwise",
-        "layers.*.self_attn.o_proj": "rowwise",
-        "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"]),
-    }
-
-    def __init__(
-        self,
-        vocab_size=151936,
-        hidden_size=4096,
-        intermediate_size=22016,
-        num_hidden_layers=32,
-        num_attention_heads=32,
-        num_key_value_heads=8,  # Match num_attention_heads for compatibility
-        head_dim=128,
-        hidden_act="silu",
-        max_position_embeddings=32768,
-        initializer_range=0.02,
-        rms_norm_eps=1e-6,
-        use_cache=True,
-        tie_word_embeddings=False,
-        rope_theta=10000.0,
-        rope_scaling=None,
-        attention_bias=False,
-        use_sliding_window=False,
-        sliding_window=4096,
-        max_window_layers=28,
-        layer_types=None,
-        attention_dropout=0.0,
-        # Advanced reasoning parameters
-        use_emotional_reasoning=False,  # Disable by default for now
-        use_perspective_threading=True,
-        num_emotion_heads=4,
-        num_thinking_stages=3,
-        emotion_hidden_size=512,
-        perspective_threads=4,
-        thinking_depth=2,
-        structured_output_vocab_size=100,
-        empathy_scaling_factor=1.2,
-        reasoning_temperature=0.8,
-        **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.use_sliding_window = use_sliding_window
-        self.sliding_window = sliding_window if self.use_sliding_window else None
-        self.max_window_layers = max_window_layers
-
-        # 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.head_dim = head_dim
-        self.hidden_act = hidden_act
-        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_bias = attention_bias
-        self.attention_dropout = attention_dropout
-        
-        # Advanced reasoning capabilities
-        self.use_emotional_reasoning = use_emotional_reasoning
-        self.use_perspective_threading = use_perspective_threading
-        self.num_emotion_heads = num_emotion_heads
-        self.num_thinking_stages = num_thinking_stages
-        self.emotion_hidden_size = emotion_hidden_size
-        self.perspective_threads = perspective_threads
-        self.thinking_depth = thinking_depth
-        self.structured_output_vocab_size = structured_output_vocab_size
-        self.empathy_scaling_factor = empathy_scaling_factor
-        self.reasoning_temperature = reasoning_temperature
-        
-        # Validate emotional reasoning parameters
-        if self.use_emotional_reasoning and self.num_emotion_heads > self.num_attention_heads:
-            raise ValueError(f"num_emotion_heads ({self.num_emotion_heads}) cannot exceed num_attention_heads ({self.num_attention_heads})")
-        
-        if self.use_perspective_threading and self.perspective_threads < 2:
-            raise ValueError(f"perspective_threads ({self.perspective_threads}) must be at least 2 for meaningful threading")
-            
-        # Validate the correctness of rotary position embeddings parameters
-        # BC: if there is a 'type' field, move it to 'rope_type'.
-        if self.rope_scaling is not None and "type" in self.rope_scaling:
-            self.rope_scaling["rope_type"] = self.rope_scaling["type"]
-        rope_config_validation(self)
-
-        self.layer_types = layer_types
-        if self.layer_types is None:
-            self.layer_types = [
-                "sliding_attention"
-                if self.sliding_window is not None and i >= self.max_window_layers
-                else "full_attention"
-                for i in range(self.num_hidden_layers)
-            ]
-        layer_type_validation(self.layer_types)
-
-        super().__init__(
-            tie_word_embeddings=tie_word_embeddings,
-            **kwargs,
-        )
-
-
-__all__ = ["HelpingAIConfig"]
-
+"""HelpingAI model configuration"""
+
+from transformers.configuration_utils import PretrainedConfig, layer_type_validation
+from transformers.modeling_rope_utils import rope_config_validation
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class HelpingAIConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`HelpingAIModel`]. It is used to instantiate a
+    HelpingAI model according to the specified arguments, defining the model architecture. Instantiating a configuration
+    with the defaults will yield a similar configuration to that of
+    HelpingAI-8B [HelpingAI/HelpingAI-8B](https://huggingface.co/HelpingAI/HelpingAI-8B).
+
+    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 151936):
+            Vocabulary size of the HelpingAI model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`HelpingAIModel`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 22016):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        num_key_value_heads (`int`, *optional*, defaults to 32):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details, check out [this
+            paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `32`.
+        head_dim (`int`, *optional*, defaults to 128):
+            The attention head dimension.
+        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 32768):
+            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-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`.
+        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. NOTE: if you apply new rope type
+            and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
+            accordingly.
+            Expected contents:
+                `rope_type` (`str`):
+                    The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
+                    'llama3'], with 'default' being the original RoPE implementation.
+                `factor` (`float`, *optional*):
+                    Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
+                    most scaling types, a `factor` of x will enable the model to handle sequences of length x *
+                    original maximum pre-trained length.
+                `original_max_position_embeddings` (`int`, *optional*):
+                    Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
+                    pretraining.
+                `attention_factor` (`float`, *optional*):
+                    Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
+                    computation. If unspecified, it defaults to value recommended by the implementation, using the
+                    `factor` field to infer the suggested value.
+                `beta_fast` (`float`, *optional*):
+                    Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
+                    ramp function. If unspecified, it defaults to 32.
+                `beta_slow` (`float`, *optional*):
+                    Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
+                    ramp function. If unspecified, it defaults to 1.
+                `short_factor` (`list[float]`, *optional*):
+                    Only used with 'longrope'. The scaling factor to be applied to short contexts (<
+                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
+                    size divided by the number of attention heads divided by 2
+                `long_factor` (`list[float]`, *optional*):
+                    Only used with 'longrope'. The scaling factor to be applied to long contexts (<
+                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
+                    size divided by the number of attention heads divided by 2
+                `low_freq_factor` (`float`, *optional*):
+                    Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
+                `high_freq_factor` (`float`, *optional*):
+                    Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
+        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.
+        use_sliding_window (`bool`, *optional*, defaults to `False`):
+            Whether to use sliding window attention.
+        sliding_window (`int`, *optional*, defaults to 4096):
+            Sliding window attention (SWA) window size. If not specified, will default to `4096`.
+        max_window_layers (`int`, *optional*, defaults to 28):
+            The number of layers using full attention. The first `max_window_layers` layers will use full attention, while any
+            additional layer afterwards will use SWA (Sliding Window Attention).
+        layer_types (`list`, *optional*):
+            Attention pattern for each layer.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        use_emotional_reasoning (`bool`, *optional*, defaults to `True`):
+            Whether to enable Semantic Emotion Reasoning (SER) capabilities for emotional understanding and processing.
+        use_perspective_threading (`bool`, *optional*, defaults to `True`):
+            Whether to enable Perspective Emotion Threading (PET) for multi-threaded emotional reasoning.
+        num_emotion_heads (`int`, *optional*, defaults to 4):
+            Number of specialized attention heads dedicated to emotional processing and reasoning.
+        num_thinking_stages (`int`, *optional*, defaults to 3):
+            Number of thinking stages for multi-stage reasoning and reflection processing.
+        emotion_hidden_size (`int`, *optional*, defaults to 512):
+            Hidden size for the emotional reasoning layers and SER processing modules.
+        perspective_threads (`int`, *optional*, defaults to 4):
+            Number of parallel perspective threads for PET processing (relatable, supportive, motivational, analytical).
+        thinking_depth (`int`, *optional*, defaults to 2):
+            Depth of thinking layers for internal reasoning and reflection processes.
+        structured_output_vocab_size (`int`, *optional*, defaults to 100):
+            Additional vocabulary size for structured output tokens like <think>, <ser>, <pet>, etc.
+        empathy_scaling_factor (`float`, *optional*, defaults to 1.2):
+            Scaling factor for empathy-related attention weights and emotional processing.
+        reasoning_temperature (`float`, *optional*, defaults to 0.8):
+            Temperature parameter for reasoning and thinking processes to balance creativity and coherence.
+        use_speech_output (`bool`, *optional*, defaults to `False`):
+            Whether to enable an additional text-to-speech head that predicts mel-spectrogram frames from hidden states.
+        speech_num_mels (`int`, *optional*, defaults to `80`):
+            Number of mel bins to predict for the speech head.
+        speech_upsample_factor (`int`, *optional*, defaults to `1`):
+            Temporal upsampling factor to expand token-level hidden states to frame-level resolution by simple repetition.
+        speech_loss_type (`str`, *optional*, defaults to `"l1"`):
+            Loss for speech supervision. One of {"l1", "mse"}.
+        speech_head_hidden_dim (`int`, *optional*, defaults to `None`):
+            Hidden dimension for the speech head MLP (hidden_size -> speech_head_hidden_dim -> num_mels).
+            If None, defaults to hidden_size // 2. Increase to scale speech head params (e.g., ~9.6k for ~50M).
+
+    ```python
+    >>> from transformers import HelpingAIModel, HelpingAIConfig
+
+    >>> # Initializing a HelpingAI style configuration with advanced reasoning
+    >>> configuration = HelpingAIConfig(
+    ...     use_emotional_reasoning=True,
+    ...     use_perspective_threading=True,
+    ...     num_emotion_heads=4,
+    ...     num_thinking_stages=3
+    ... )
+
+    >>> # Initializing a model from the HelpingAI-8B style configuration
+    >>> model = HelpingAIModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "helpingai"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    # Default tensor parallel plan for base model `HelpingAI`
+    base_model_tp_plan = {
+        "layers.*.self_attn.q_proj": "colwise",
+        "layers.*.self_attn.k_proj": "colwise",
+        "layers.*.self_attn.v_proj": "colwise",
+        "layers.*.self_attn.o_proj": "rowwise",
+        "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"]),
+    }
+
+    def __init__(
+        self,
+        vocab_size=151936,
+        hidden_size=4096,
+        intermediate_size=22016,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=8,  # Match num_attention_heads for compatibility
+        head_dim=128,
+        hidden_act="silu",
+        max_position_embeddings=32768,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        attention_bias=False,
+        use_sliding_window=False,
+        sliding_window=4096,
+        max_window_layers=28,
+        layer_types=None,
+        attention_dropout=0.0,
+        # Advanced reasoning parameters
+        use_emotional_reasoning=False,  # Disable by default for now
+        use_perspective_threading=True,
+        num_emotion_heads=4,
+        num_thinking_stages=3,
+        emotion_hidden_size=512,
+        perspective_threads=4,
+        thinking_depth=2,
+        structured_output_vocab_size=100,
+        empathy_scaling_factor=1.2,
+        reasoning_temperature=0.8,
+        # Speech output head options
+        use_speech_output=False,
+        speech_num_mels=80,
+        speech_upsample_factor=1,
+        speech_loss_type="l1",
+        speech_head_hidden_dim=None,
+        **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.use_sliding_window = use_sliding_window
+        self.sliding_window = sliding_window if self.use_sliding_window else None
+        self.max_window_layers = max_window_layers
+
+        # 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.head_dim = head_dim
+        self.hidden_act = hidden_act
+        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_bias = attention_bias
+        self.attention_dropout = attention_dropout
+        
+        # Advanced reasoning capabilities
+        self.use_emotional_reasoning = use_emotional_reasoning
+        self.use_perspective_threading = use_perspective_threading
+        self.num_emotion_heads = num_emotion_heads
+        self.num_thinking_stages = num_thinking_stages
+        self.emotion_hidden_size = emotion_hidden_size
+        self.perspective_threads = perspective_threads
+        self.thinking_depth = thinking_depth
+        self.structured_output_vocab_size = structured_output_vocab_size
+        self.empathy_scaling_factor = empathy_scaling_factor
+        self.reasoning_temperature = reasoning_temperature
+        # Speech head config
+        self.use_speech_output = use_speech_output
+        self.speech_num_mels = speech_num_mels
+        self.speech_upsample_factor = speech_upsample_factor
+        self.speech_loss_type = speech_loss_type
+        self.speech_head_hidden_dim = speech_head_hidden_dim
+            
+        # Validate emotional reasoning parameters
+        if self.use_emotional_reasoning and self.num_emotion_heads > self.num_attention_heads:
+            raise ValueError(f"num_emotion_heads ({self.num_emotion_heads}) cannot exceed num_attention_heads ({self.num_attention_heads})")
+        
+        if self.use_perspective_threading and self.perspective_threads < 2:
+            raise ValueError(f"perspective_threads ({self.perspective_threads}) must be at least 2 for meaningful threading")
+        if self.use_speech_output:
+            if not isinstance(self.speech_num_mels, int) or self.speech_num_mels <= 0:
+                raise ValueError("speech_num_mels must be a positive integer")
+            if not isinstance(self.speech_upsample_factor, int) or self.speech_upsample_factor <= 0:
+                raise ValueError("speech_upsample_factor must be a positive integer")
+            if self.speech_loss_type not in {"l1", "mse"}:
+                raise ValueError("speech_loss_type must be one of {'l1','mse'}")
+            if self.speech_head_hidden_dim is not None:
+                if not isinstance(self.speech_head_hidden_dim, int) or self.speech_head_hidden_dim <= 0:
+                    raise ValueError("speech_head_hidden_dim must be a positive integer when provided")
+            
+        # Validate the correctness of rotary position embeddings parameters
+        # BC: if there is a 'type' field, move it to 'rope_type'.
+        if self.rope_scaling is not None and "type" in self.rope_scaling:
+            self.rope_scaling["rope_type"] = self.rope_scaling["type"]
+        rope_config_validation(self)
+
+        self.layer_types = layer_types
+        if self.layer_types is None:
+            self.layer_types = [
+                "sliding_attention"
+                if self.sliding_window is not None and i >= self.max_window_layers
+                else "full_attention"
+                for i in range(self.num_hidden_layers)
+            ]
+        layer_type_validation(self.layer_types)
+
+        super().__init__(
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+
+__all__ = ["HelpingAIConfig"]
+