easydel.modules.mistral.mistral_configuration

class easydel.modules.mistral.mistral_configuration.MistralConfig(vocab_size: int = 32000, hidden_size: int = 4096, intermediate_size: int = 14336, head_dim: int = 128, num_hidden_layers: int = 32, num_attention_heads: int = 32, num_key_value_heads: int = 8, hidden_act='silu', max_position_embeddings=131072, initializer_range=0.02, rms_norm_eps=1e-06, use_cache=True, pad_token_id=None, bos_token_id: int = 1, eos_token_id: int = 2, tie_word_embeddings=False, rope_theta=10000.0, rope_scaling: Dict[str, Union[str, float]] = None, sliding_window=4096, gradient_checkpointing: EasyDeLGradientCheckPointers = EasyDeLGradientCheckPointers.NONE, number_rep_kv: int = 1, attention_dropout: float = 0.0, use_scan_mlp: bool = False, scan_mlp_chunk_size: int = 1024, bits: Optional[int] = None, attention_bias: bool = False, **kwargs)

Bases: EasyDeLBaseConfig

Configuration objects inherit from [EasyDeLBaseConfig] and can be used to control the model outputs. Read the documentation from [EasyDeLBaseConfig] for more information.

Parameters

• vocab_size (int, optional, defaults to 32000) – Vocabulary size of the Mistral model. Defines the number of different tokens that can be represented by the inputs_ids passed to the forward method.

• hidden_size (int, optional, defaults to 4096) – Dimensionality of the encoder layers and the pooler layer.

• intermediate_size (int, optional, defaults to 14336) – Dimensionality of the “intermediate” (i.e., feed-forward) layer in the Transformer encoder.

• head_dim (int, defaults to 128) – Dimensionality of the head for attention.

• 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 8) – Number of key and value 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) to use in the encoder and pooler. If string, “gelu”, “relu”, “swish” and “gelu_new” are supported.

• max_position_embeddings (int, optional, defaults to 4096 * 32) – The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 2048 or 4096).

• 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 (not used by all models). Only relevant if config.is_decoder=True.

• pad_token_id (int, optional) – The index of the padding token in the vocabulary.

• 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 to tie the weights of the input embeddings and the output embeddings.

• rope_theta (float, optional, defaults to 10000.0) – The theta value to use for rotary position embeddings.

• rope_scaling (tp.Dict[str, tp.Union[str, float]], optional) – The configuration for rope scaling.

• sliding_window (int, optional, defaults to 4096) – The sliding window size.

• gradient_checkpointing (str, optional, defaults to “nothing_saveable”) – The gradient checkpointing configuration.

• number_rep_kv (int, optional, defaults to 1) – Number of repetitions for the key and value vectors.

• attention_dropout (float, optional, defaults to 0.0) – The dropout ratio for the attention probabilities.

• use_scan_mlp (bool, optional, defaults to False) – Whether to use the scan implementation for the MLP.

• scan_mlp_chunk_size (int, optional, defaults to 1024) – The chunk size to use when scanning the MLP.

• bits (int, optional) – The number of bits to quantize the model to.

• attention_bias (bool, optional, defaults to False) – Whether to use bias in the attention layer.

attach_custom_arguments(gradient_checkpointing: EasyDeLGradientCheckPointers = EasyDeLGradientCheckPointers.NONE, use_scan_mlp: bool = False, scan_mlp_chunk_size: int = 1024, number_rep_kv: int = 1, bits: Optional[int] = None, attention_dropout: float = 0.0, rope_scaling: Dict[str, Union[str, float]] = None, attention_bias: bool = False, **kwargs)

The attach_custom_arguments function adds the following arguments to the model:

Parameters

• self – Bind the attributes and methods of a class to an instance of that class

• gradient_checkpointing – str: Determine whether to use gradient checkpointing

• use_scan_mlp – bool: Determine whether to use the scan_mlp function or notn

• scan_mlp_chunk_size – int: Chunk the input to the mlp

• number_rep_kv – int: Control the number of times that the key and value vectors are repeated

• bits – tp.Optional[int]: Specify the number of bits to use for quantization

• attention_dropout – float: Set the dropout rate for the attention layer

• attention_bias – bool: when ever to use attention_bias

• rope_scaling – tp.Dict[str, tp.Union[str, float]]: rope_scaling for rope

Return type

A tuple of the following

get_partition_rules(*args, **kwargs)

Get the partition rules for the model. :returns: The partition rules. :rtype: tp.Tuple[tp.Tuple[str, PartitionSpec]]

static get_weight_decay_exclusions()

property granted_freq_max_position_embedding: int

property granted_mask_max_position_embedding: int

model_type: str = 'mistral'

static rng_keys()