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examples/modular-transformers/configuration_new_model.py

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+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from examples/modular-transformers/modular_new_model.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_new_model.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# Example where we only want to overwrite the defaults of an init
+
+from huggingface_hub.dataclasses import strict
+
+from ...configuration_utils import PreTrainedConfig
+from ...utils import auto_docstring
+
+
+@auto_docstring(checkpoint="google/new_model-7b")
+@strict
+class NewModelConfig(PreTrainedConfig):
+    r"""
+    use_bidirectional_attention (`bool`, *optional*):
+        If True, the model will attend to all text tokens instead of using a causal mask.
+
+    ```python
+    >>> from transformers import NewModelModel, NewModelConfig
+    >>> # Initializing a NewModel new_model-7b style configuration
+    >>> configuration = NewModelConfig()
+    >>> # Initializing a model from the new_model-7b style configuration
+    >>> model = NewModelModel(configuration)
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "new_model"
+    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.gate_proj": "colwise",
+        "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"]),
+    }
+    vocab_size: int = 256030
+    hidden_size: int = 64
+    intermediate_size: int = 90
+    num_hidden_layers: int = 28
+    num_attention_heads: int = 16
+    num_key_value_heads: int = 16
+    head_dim: int = 256
+    hidden_act: str = "gelu_pytorch_tanh"
+    max_position_embeddings: int = 1500
+    initializer_range: float = 0.02
+    rms_norm_eps: float = 1e-6
+    use_cache: bool = True
+    pad_token_id: int = 0
+    eos_token_id: int = 1
+    bos_token_id: int = 2
+    tie_word_embeddings: bool = True
+    rope_parameters: dict | None = None
+    attention_bias: bool = False
+    attention_dropout: float = 0.0
+    use_bidirectional_attention: bool = False
+    hidden_activation: str | None = None
+
+    @property
+    def num_heads(self):
+        return self.num_attention_heads