scripts/ft_gemma3n_audio_vt.py [56:76]:
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    labels[labels == processor.tokenizer.image_token_id] = -100
    labels[labels == processor.tokenizer.boi_token_id] = -100
    labels[labels == processor.tokenizer.eoi_token_id] = -100

    batch["labels"] = labels

    return batch


def freeze_layers(model):
    for name, param in model.named_parameters():
        if "attn" in name:
            param.requires_grad = True
        else:
            param.requires_grad = False
    return model


def run_inference(val_dataset, processor, model, fname):
    # infer before training
    val_sample = random.choice(val_dataset)
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scripts/ft_gemma3n_image_vt.py [58:78]:
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    labels[labels == processor.tokenizer.image_token_id] = -100
    labels[labels == processor.tokenizer.boi_token_id] = -100
    labels[labels == processor.tokenizer.eoi_token_id] = -100

    batch["labels"] = labels

    return batch


def freeze_layers(model):
    for name, param in model.named_parameters():
        if "attn" in name:
            param.requires_grad = True
        else:
            param.requires_grad = False
    return model


def run_inference(val_dataset, processor, model, fname):
    # infer before training
    val_sample = random.choice(val_dataset)
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