Class InternLMConfig

tools/transformers/configuration_internlm.py:31–120

r""" This is the configuration class to store the configuration of a [`InternLMModel`]. It is used to instantiate an InternLM model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to

class InternLMConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`InternLMModel`]. It is used to instantiate an InternLM
    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 InternLM-7B.

    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 InternLM model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`InternLMModel`]
        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 Transformer encoder.
        num_attention_heads (`int`, *optional*, defaults to 32):
            Number of attention heads for each attention layer in the Transformer encoder.
        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 2048):
            The maximum sequence length that this model might ever be used with. Typically set this to something large
            just in case (e.g., 512 or 1024 or 2048).
        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-12):
            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 to tie weight embeddings
        Example:

    ```python
    >>> from transformers import InternLMModel, InternLMConfig

    >>> # Initializing a InternLM internlm-7b style configuration
    >>> configuration = InternLMConfig()

    >>> # Initializing a model from the internlm-7b style configuration
    >>> model = InternLMModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""
    model_type = "internlm"
    _auto_class = "AutoConfig"

    def __init__(
        self,
        vocab_size=103168,
        hidden_size=4096,
        intermediate_size=11008,