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fix: checkpoint conversion to HF #285

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fix: checkpoint conversion to HF #285

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5 changes: 3 additions & 2 deletions src/modalities/models/huggingface_adapters/hf_adapter.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,7 +17,7 @@ class HFModelAdapterConfig(PretrainedConfig):

model_type = "modalities"

def __init__(self, **kwargs):
def __init__(self, config={}, **kwargs):
"""
Initializes an HFModelAdapterConfig object.

Expand All @@ -28,6 +28,7 @@ def __init__(self, **kwargs):
ConfigError: If the config is not passed in HFModelAdapterConfig.
"""
super().__init__(**kwargs)
self.config = config
# self.config is added by the super class via kwargs
if self.config is None:
raise ConfigError("Config is not passed in HFModelAdapterConfig.")
Expand Down Expand Up @@ -115,7 +116,7 @@ def forward(
raise NotImplementedError
model_input = {"input_ids": input_ids, "attention_mask": attention_mask}
model_forward_output: dict[str, torch.Tensor] = self.model.forward(model_input)
if return_dict:
if not return_dict:
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I executed the checkpoint conversion. Everything works perfectly.

I ran eval harness with these changes (since the HFModelAdapterConfig and HFModelAdapter are required while evaluating modalities using eval harness as well). This change seems to have affected the eval run. On reverting this change back, it seems to run.

return ModalitiesModelOutput(**model_forward_output)
else:
return model_forward_output[self.prediction_key]
Expand Down
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