# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # This file was automatically generated from src/transformers/models/jais2/modular_jais2.py. # Do NOT edit this file manually as any edits will be overwritten by the generation of # the file from the modular. If any change should be done, please apply the change to the # modular_jais2.py file directly. One of our CI enforces this. # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # Copyright 2025 the HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from ...configuration_utils import PreTrainedConfig from ...modeling_rope_utils import RopeParameters class Jais2Config(PreTrainedConfig): r""" This is the configuration class to store the configuration of a [`Jais2Model`]. It is used to instantiate a Jais2 model according to the specified arguments, defining the model architecture. [inceptionai/Jais-2-8B-Chat](https://huggingface.co/inceptionai/Jais-2-8B-Chat). 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 150272): Vocabulary size of the Jais2 model. hidden_size (`int`, *optional*, defaults to 3328): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 26624): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 32): Number of hidden layers in the Transformer decoder. num_attention_heads (`int`, *optional*, defaults to 26): Number of attention heads for each attention layer. num_key_value_heads (`int`, *optional*): Number of key_value heads for Grouped Query Attention. hidden_act (`str`, *optional*, defaults to `"relu2"`): The non-linear activation function in the decoder. max_position_embeddings (`int`, *optional*, defaults to 8192): The maximum sequence length. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer. layer_norm_eps (`float`, *optional*, defaults to 1e-05): The epsilon used by the normalization layers. use_cache (`bool`, *optional*, defaults to `True`): Whether to return last key/values attentions. pad_token_id (`int`, *optional*): Padding token id. bos_token_id (`int`, *optional*, defaults to 0): Beginning of stream token id. eos_token_id (`int`, *optional*, defaults to 150024): End of stream token id. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether to tie weight embeddings. attention_bias (`bool`, *optional*, defaults to `True`): Whether to use a bias in the query, key, value and output projection layers. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. mlp_bias (`bool`, *optional*, defaults to `True`): Whether to use a bias in up_proj, down_proj and gate_proj layers. head_dim (`int`, *optional*): The attention head dimension. rope_parameters (`dict`, *optional*): The RoPE parameters. """ model_type = "jais2" keys_to_ignore_at_inference = ["past_key_values"] 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.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: int | None = 150272, hidden_size: int | None = 3328, intermediate_size: int | None = 26624, num_hidden_layers: int | None = 32, num_attention_heads: int | None = 26, num_key_value_heads: int | None = None, hidden_act: str | None = "relu2", max_position_embeddings: int | None = 8192, initializer_range: float | None = 0.02, layer_norm_eps: float | None = 1e-5, use_cache: bool | None = True, pad_token_id: int | None = None, bos_token_id: int | None = 0, eos_token_id: int | None = 150024, tie_word_embeddings: bool | None = False, attention_bias: bool | None = True, attention_dropout: float | None = 0.0, mlp_bias: bool | None = True, head_dim: int | None = None, rope_parameters: RopeParameters | dict[str, RopeParameters] | None = 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 # 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.hidden_act = hidden_act self.initializer_range = initializer_range self.use_cache = use_cache self.attention_bias = attention_bias self.attention_dropout = attention_dropout self.mlp_bias = mlp_bias self.head_dim = head_dim if head_dim is not None else self.hidden_size // self.num_attention_heads self.rope_parameters = rope_parameters self.tie_word_embeddings = tie_word_embeddings self.pad_token_id = pad_token_id self.bos_token_id = bos_token_id self.eos_token_id = eos_token_id super().__init__(**kwargs) self.layer_norm_eps = layer_norm_eps __all__ = ["Jais2Config"]