diff --git a/pgml-extension/src/bindings/transformers/transformers.py b/pgml-extension/src/bindings/transformers/transformers.py
index 83d953fdd..7bcecc8cc 100644
--- a/pgml-extension/src/bindings/transformers/transformers.py
+++ b/pgml-extension/src/bindings/transformers/transformers.py
@@ -41,6 +41,7 @@
PegasusTokenizer,
TrainingArguments,
Trainer,
+ GPTQConfig
)
import threading
@@ -279,7 +280,13 @@ def __init__(self, model_name, **kwargs):
elif self.task == "summarization" or self.task == "translation":
self.model = AutoModelForSeq2SeqLM.from_pretrained(model_name, **kwargs)
elif self.task == "text-generation" or self.task == "conversational":
- self.model = AutoModelForCausalLM.from_pretrained(model_name, **kwargs)
+ # See: https://huggingface.co/docs/transformers/main/quantization
+ if "quantization_config" in kwargs:
+ quantization_config = kwargs.pop("quantization_config")
+ quantization_config = GPTQConfig(**quantization_config)
+ self.model = AutoModelForCausalLM.from_pretrained(model_name, quantization_config=quantization_config, **kwargs)
+ else:
+ self.model = AutoModelForCausalLM.from_pretrained(model_name, **kwargs)
else:
raise PgMLException(f"Unhandled task: {self.task}")
@@ -409,10 +416,13 @@ def create_pipeline(task):
else:
try:
pipe = StandardPipeline(model_name, **task)
- except TypeError:
- # some models fail when given "device" kwargs, remove and try again
- task.pop("device")
- pipe = StandardPipeline(model_name, **task)
+ except TypeError as error:
+ if "device" in task:
+ # some models fail when given "device" kwargs, remove and try again
+ task.pop("device")
+ pipe = StandardPipeline(model_name, **task)
+ else:
+ raise error
return pipe
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