easydel.modules.minimax_text_v1.__init__

easydel.modules.minimax_text_v1.__init__#

class easydel.modules.minimax_text_v1.__init__.MiniMaxText01Config(vocab_size=32000, hidden_size=4096, intermediate_size=14336, num_hidden_layers=32, num_attention_heads=32, num_key_value_heads=8, hidden_act='silu', max_position_embeddings=131072, initializer_range=0.02, rms_norm_eps=1e-05, use_cache=True, pad_token_id=None, bos_token_id=None, eos_token_id=None, tie_word_embeddings=False, rope_theta=1000000.0, sliding_window=None, attention_dropout=0.0, num_experts_per_tok=2, num_local_experts=8, output_router_logits=False, router_aux_loss_coef=0.001, router_jitter_noise=0.0, **kwargs)[source]#

Bases: EasyDeLBaseConfig

This is the configuration class to store the configuration of a [MiniMaxText01Model]. It is used to instantiate an MiniMaxText01 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 MiniMaxText01. Configuration objects inherit from [PretrainedConfig] and can be used to control the model outputs. Read the documentation from [PretrainedConfig] for more information. :param vocab_size: Vocabulary size of the MiniMaxText01 model. Defines the number of different tokens that can be represented by the

inputs_ids passed when calling [MiniMaxText01Model]

Parameters

  • hidden_size (int, optional, defaults to 4096) – Dimension of the hidden representations.

  • intermediate_size (int, optional, defaults to 14336) – 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 8) – 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 checkout [this paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `8.

  • 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 4096*32) – The maximum sequence length that this model might ever be used with. MiniMaxText01’s sliding window attention allows sequence of up to 4096*32 tokens.

  • 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-05) – 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 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 1000000.0) – The base period of the RoPE embeddings.

  • sliding_window (int, optional) – Sliding window attention window size. If not specified, will default to 4096.

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

  • num_experts_per_tok (int, optional, defaults to 2) – The number of experts to route per-token, can be also interpreted as the top-k routing parameter

  • num_local_experts (int, optional, defaults to 8) – Number of experts per Sparse MLP layer.

  • output_router_logits (bool, optional, defaults to False) – Whether or not the router logits should be returned by the model. Enabeling this will also allow the model to output the auxiliary loss. See [here]() for more details

  • router_aux_loss_coef (float, optional, defaults to 0.001) – The aux loss factor for the total loss.

  • router_jitter_noise (float, optional, defaults to 0.0) – Amount of noise to add to the router.

`python >>> from transformers import MiniMaxText01Model, MiniMaxText01Config >>> # Initializing a MiniMaxText01 style configuration >>> configuration = MiniMaxText01Config() >>> # Initializing a model from the MiniMaxText01 style configuration >>> model = MiniMaxText01Model(configuration) >>> # Accessing the model configuration >>> configuration = model.config `

keys_to_ignore_at_inference = ['past_key_values']#
model_type: str = 'MiniMaxText01'#
class easydel.modules.minimax_text_v1.__init__.MiniMaxText01ForCausalLM(*args: Any, **kwargs: Any)[source]#

Bases: EasyDeLBaseModule

class easydel.modules.minimax_text_v1.__init__.MiniMaxText01Model(*args: Any, **kwargs: Any)[source]#

Bases: EasyDeLBaseModule

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