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Model: IQuestLab/IQuest-Coder-V1-7B-Base Source: Original Platform
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ModelHub XC
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configuration_iquestcoder.py
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182
configuration_iquestcoder.py
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"""IQuestCoder model configuration."""
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from transformers.configuration_utils import PretrainedConfig
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from transformers.utils import logging
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logger = logging.get_logger(__name__)
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class IQuestCoderConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`IQuestCoderModel`]. It is used to instantiate
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an IQuestCoder model according to the specified arguments, defining the model architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 76800):
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Vocabulary size of the IQuestCoder model. Defines the number of different tokens that can be represented
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by the `inputs_ids` passed when calling [`IQuestCoderModel`].
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hidden_size (`int`, *optional*, defaults to 5120):
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Dimension of the hidden representations.
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intermediate_size (`int`, *optional*, defaults to 27648):
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Dimension of the MLP representations.
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num_hidden_layers (`int`, *optional*, defaults to 80):
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Number of hidden layers in the Transformer decoder.
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num_attention_heads (`int`, *optional*, defaults to 40):
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Number of attention heads for each attention layer in the Transformer decoder.
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num_key_value_heads (`int`, *optional*, defaults to 8):
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This is the number of key_value heads that should be used to implement Grouped Query Attention (GQA).
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If `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA).
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If `num_key_value_heads=1`, the model will use Multi Query Attention (MQA).
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head_dim (`int`, *optional*, defaults to 128):
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The dimension of each attention head. If not specified, defaults to `hidden_size // num_attention_heads`.
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hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
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The non-linear activation function (function or string) in the decoder.
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max_position_embeddings (`int`, *optional*, defaults to 16384):
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The maximum sequence length that this model might ever be used with.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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rms_norm_eps (`float`, *optional*, defaults to 1e-05):
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The epsilon used by the rms normalization layers.
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether or not the model should return the last key/values attentions (not used by all models).
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pad_token_id (`int`, *optional*):
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Padding token id.
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bos_token_id (`int`, *optional*, defaults to 1):
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Beginning of stream token id.
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eos_token_id (`int`, *optional*, defaults to 2):
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End of stream token id.
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tie_word_embeddings (`bool`, *optional*, defaults to `False`):
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Whether to tie weight embeddings.
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rope_theta (`float`, *optional*, defaults to 500000.0):
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The base period of the RoPE embeddings.
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rope_scaling (`Dict`, *optional*):
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Dictionary containing the scaling configuration for the RoPE embeddings. Supports various RoPE scaling
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types including "linear", "dynamic", "yarn", "longrope", etc.
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attention_bias (`bool`, *optional*, defaults to `False`):
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Whether to use a bias in the query, key, value and output projection layers during self-attention.
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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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mlp_bias (`bool`, *optional*, defaults to `False`):
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Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers.
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clip_qkv (`float`, *optional*):
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If set, clip the query, key, and value tensors to this value. Borrowed from OLMo for training stability.
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use_sliding_window (`bool`, *optional*, defaults to `False`):
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Whether to use sliding window attention. Borrowed from Qwen2.
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sliding_window (`int`, *optional*):
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The sliding window size. Only effective when `use_sliding_window=True`.
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max_window_layers (`int`, *optional*, defaults to 0):
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The number of layers that don't use sliding window attention. Borrowed from Qwen2.
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Example:
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```python
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>>> from configuration_iquestcoder import IQuestCoderConfig
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>>> from modeling_iquestcoder import IQuestCoderModel
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>>> # Initializing a IQuestCoder configuration
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>>> configuration = IQuestCoderConfig()
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>>> # Initializing a model from the configuration
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>>> model = IQuestCoderModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```
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"""
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model_type = "iquestcoder"
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keys_to_ignore_at_inference = ["past_key_values"]
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def __init__(
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self,
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vocab_size=76800,
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hidden_size=5120,
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intermediate_size=27648,
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num_hidden_layers=80,
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num_attention_heads=40,
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num_key_value_heads=8,
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head_dim=128,
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hidden_act="silu",
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max_position_embeddings=16384,
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initializer_range=0.02,
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rms_norm_eps=1e-5,
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use_cache=True,
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pad_token_id=None,
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bos_token_id=1,
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eos_token_id=2,
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tie_word_embeddings=False,
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rope_theta=500000.0,
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rope_scaling=None,
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attention_bias=False,
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attention_dropout=0.0,
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mlp_bias=False,
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# IQuestCoder specific (borrowed from OLMo)
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clip_qkv=None,
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# IQuestCoder specific (borrowed from Qwen2)
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use_sliding_window=False,
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sliding_window=None,
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max_window_layers=0,
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**kwargs,
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):
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self.vocab_size = vocab_size
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self.max_position_embeddings = max_position_embeddings
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.num_key_value_heads = num_key_value_heads
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self.head_dim = head_dim
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self.hidden_act = hidden_act
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self.initializer_range = initializer_range
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self.rms_norm_eps = rms_norm_eps
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self.use_cache = use_cache
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self.rope_theta = rope_theta
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self.rope_scaling = rope_scaling
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self.attention_bias = attention_bias
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self.attention_dropout = attention_dropout
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self.mlp_bias = mlp_bias
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# IQuestCoder specific
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self.clip_qkv = clip_qkv
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self.use_sliding_window = use_sliding_window
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self.sliding_window = sliding_window
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self.max_window_layers = max_window_layers
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# Validate rope_scaling configuration
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self._rope_scaling_validation()
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super().__init__(
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pad_token_id=pad_token_id,
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bos_token_id=bos_token_id,
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eos_token_id=eos_token_id,
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tie_word_embeddings=tie_word_embeddings,
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**kwargs,
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)
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def _rope_scaling_validation(self):
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"""Validate the `rope_scaling` configuration."""
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if self.rope_scaling is None:
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return
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if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) < 1:
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raise ValueError(
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"`rope_scaling` must be a dictionary with a minimum of one field, `type` or `rope_type`."
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)
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rope_scaling_type = self.rope_scaling.get("type", None) or self.rope_scaling.get("rope_type", None)
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if rope_scaling_type is None:
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raise ValueError(
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"`rope_scaling` must have a `type` or `rope_type` field."
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)
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valid_rope_types = ["linear", "dynamic", "yarn", "longrope", "llama3"]
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if rope_scaling_type not in valid_rope_types:
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raise ValueError(
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f"`rope_scaling`'s type field must be one of {valid_rope_types}, got {rope_scaling_type}"
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)
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__all__ = ["IQuestCoderConfig"]
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