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73 lines
3.0 KiB
Python
73 lines
3.0 KiB
Python
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# This file was automatically generated from examples/modular-transformers/modular_new_model.py.
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# Do NOT edit this file manually as any edits will be overwritten by the generation of
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# the file from the modular. If any change should be done, please apply the change to the
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# modular_new_model.py file directly. One of our CI enforces this.
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# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# Example where we only want to overwrite the defaults of an init
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from huggingface_hub.dataclasses import strict
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from ...configuration_utils import PreTrainedConfig
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from ...utils import auto_docstring
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@auto_docstring(checkpoint="google/new_model-7b")
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@strict
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class NewModelConfig(PreTrainedConfig):
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r"""
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use_bidirectional_attention (`bool`, *optional*):
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If True, the model will attend to all text tokens instead of using a causal mask.
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```python
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>>> from transformers import NewModelModel, NewModelConfig
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>>> # Initializing a NewModel new_model-7b style configuration
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>>> configuration = NewModelConfig()
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>>> # Initializing a model from the new_model-7b style configuration
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>>> model = NewModelModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "new_model"
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keys_to_ignore_at_inference = ["past_key_values"]
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base_model_tp_plan = {
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"layers.*.self_attn.q_proj": "colwise",
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"layers.*.self_attn.k_proj": "colwise",
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"layers.*.self_attn.v_proj": "colwise",
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"layers.*.self_attn.o_proj": "rowwise",
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"layers.*.mlp.gate_proj": "colwise",
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"layers.*.mlp.up_proj": "colwise",
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"layers.*.mlp.down_proj": "rowwise",
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}
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base_model_pp_plan = {
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"embed_tokens": (["input_ids"], ["inputs_embeds"]),
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"layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
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"norm": (["hidden_states"], ["hidden_states"]),
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}
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vocab_size: int = 256030
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hidden_size: int = 64
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intermediate_size: int = 90
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num_hidden_layers: int = 28
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num_attention_heads: int = 16
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num_key_value_heads: int = 16
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head_dim: int = 256
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hidden_act: str = "gelu_pytorch_tanh"
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max_position_embeddings: int = 1500
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initializer_range: float = 0.02
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rms_norm_eps: float = 1e-6
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use_cache: bool = True
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pad_token_id: int = 0
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eos_token_id: int = 1
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bos_token_id: int = 2
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tie_word_embeddings: bool = True
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rope_parameters: dict | None = None
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attention_bias: bool = False
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attention_dropout: float = 0.0
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use_bidirectional_attention: bool = False
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hidden_activation: str | None = None
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@property
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def num_heads(self):
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return self.num_attention_heads
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