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fixed nn to_device() issues #25

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Merged
akshithambekar merged 1 commit into from
Sep 24, 2025
Merged

fixed nn to_device() issues #25

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6 changes: 5 additions & 1 deletion tueri/output_scanners/factual_consistency.py
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Original file line number Diff line number Diff line change
Expand Up @@ -50,7 +50,11 @@ def __init__(
use_onnx=use_onnx,
)
if not use_onnx:
self._model = self._model.to(device())
# Handle meta device properly - use to_empty() when moving from meta device
if hasattr(self._model, 'device') and str(self._model.device) == 'meta':
self._model = self._model.to_empty(device=device())
else:
self._model = self._model.to(device())
self._model.eval()

def scan(self, prompt: str, output: str) -> tuple[str, bool, float]:
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7 changes: 6 additions & 1 deletion tueri/output_scanners/relevance.py
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Original file line number Diff line number Diff line change
Expand Up @@ -110,7 +110,12 @@ def __init__(
subfolder=model.subfolder,
revision=model.revision,
**model.kwargs,
).to(device())
)
# Handle meta device properly - use to_empty() when moving from meta device
if hasattr(self._model, 'device') and str(self._model.device) == 'meta':
self._model = self._model.to_empty(device=device())
else:
self._model = self._model.to(device())
LOGGER.debug("Initialized model", model=model, device=device())
self._model.eval()

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12 changes: 10 additions & 2 deletions tueri/transformers_helpers.py
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Original file line number Diff line number Diff line change
Expand Up @@ -93,7 +93,11 @@ def get_tokenizer_and_model_for_classification(
low_cpu_mem_usage=False,
**model.kwargs,
)
tf_model = tf_model.to(device())
# Handle meta device properly - use to_empty() when moving from meta device
if hasattr(tf_model, 'device') and str(tf_model.device) == 'meta':
tf_model = tf_model.to_empty(device=device())
else:
tf_model = tf_model.to(device())
LOGGER.debug("Initialized classification model", model=model, device=device())

return tf_tokenizer, tf_model
Expand Down Expand Up @@ -131,7 +135,11 @@ def get_tokenizer_and_model_for_ner(
low_cpu_mem_usage=False,
**model.kwargs,
)
tf_model = tf_model.to(device())
# Handle meta device properly - use to_empty() when moving from meta device
if hasattr(tf_model, 'device') and str(tf_model.device) == 'meta':
tf_model = tf_model.to_empty(device=device())
else:
tf_model = tf_model.to(device())
LOGGER.debug("Initialized NER model", model=model, device=device())

return tf_tokenizer, tf_model
Expand Down