poprawka trener c.d.
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hft.py
5
hft.py
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@ -99,13 +99,14 @@ class CustomModel(AutoModelForCausalLM):
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# Dostosowany Trainer
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class CustomTrainer(Trainer):
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def compute_loss(self, model, inputs, return_outputs=False, num_items_in_batch=None):
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def compute_loss(self, model, inputs, return_outputs=False):
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labels = inputs.pop("labels")
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source = inputs.pop("source")
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source = inputs.pop("source", None) # Użyj None jako wartości domyślnej
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outputs = model(**inputs, labels=labels)
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loss = outputs.loss
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return (loss, outputs) if return_outputs else loss
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# Przygotowanie modelu i tokenizera
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model_name = "google/gemma-2-2b"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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