precommit
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@@ -82,10 +82,10 @@ class SmolVLAConfig(PreTrainedConfig):
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scheduler_decay_steps: int = 30_000
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scheduler_decay_lr: float = 2.5e-6
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vlm_model_name: str = "HuggingFaceTB/SmolVLM2-500M-Video-Instruct" # Select the VLM backbone.
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load_vlm_weights: bool = False # Set to True in case of training the expert from scratch. True when init from pretrained SmolVLA weights
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vlm_model_name: str = "HuggingFaceTB/SmolVLM2-500M-Video-Instruct" # Select the VLM backbone.
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load_vlm_weights: bool = False # Set to True in case of training the expert from scratch. True when init from pretrained SmolVLA weights
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add_image_special_tokens: bool = False # Whether to use special image tokens around image features.
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add_image_special_tokens: bool = False # Whether to use special image tokens around image features.
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attention_mode: str = "cross_attn"
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@@ -93,10 +93,10 @@ class SmolVLAConfig(PreTrainedConfig):
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pad_language_to: str = "longest" # "max_length"
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num_expert_layers: int = -1 # Less or equal to 0 is the default where the action expert has the same number of layers of VLM. Otherwise the expert have less layers.
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num_vlm_layers: int = 16 # Number of layers used in the VLM (first num_vlm_layers layers)
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self_attn_every_n_layers: int = 2 # Interleave SA layers each self_attn_every_n_layers
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expert_width_multiplier: float = 0.75 # The action expert hidden size (wrt to the VLM)
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num_expert_layers: int = -1 # Less or equal to 0 is the default where the action expert has the same number of layers of VLM. Otherwise the expert have less layers.
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num_vlm_layers: int = 16 # Number of layers used in the VLM (first num_vlm_layers layers)
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self_attn_every_n_layers: int = 2 # Interleave SA layers each self_attn_every_n_layers
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expert_width_multiplier: float = 0.75 # The action expert hidden size (wrt to the VLM)
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def __post_init__(self):
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super().__post_init__()
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