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@ -8,14 +8,6 @@ from modules.logging_colors import logger
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from modules.models import reload_model
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from modules.models import reload_model
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def merge_loras():
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if len(list({shared.model.peft_config[adapter].r for adapter in shared.model.peft_config.keys()})) > 1:
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logger.warning("The loaded LoRAs cannot be merged, as they have dissimilar ranks. Only the first one will be active.")
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return
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shared.model.add_weighted_adapter(shared.lora_names, [1] * len(shared.lora_names), "__merged")
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shared.model.set_adapter("__merged")
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def add_lora_to_model(lora_names):
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def add_lora_to_model(lora_names):
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if 'GPTQForCausalLM' in shared.model.__class__.__name__ or shared.args.loader == 'AutoGPTQ':
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if 'GPTQForCausalLM' in shared.model.__class__.__name__ or shared.args.loader == 'AutoGPTQ':
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add_lora_autogptq(lora_names)
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add_lora_autogptq(lora_names)
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@ -189,3 +181,12 @@ def add_lora_transformers(lora_names):
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shared.model = shared.model.to(device)
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shared.model = shared.model.to(device)
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else:
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else:
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shared.model = shared.model.cuda()
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shared.model = shared.model.cuda()
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def merge_loras():
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if len(list({shared.model.peft_config[adapter].r for adapter in shared.model.peft_config.keys()})) > 1:
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logger.warning("The loaded LoRAs cannot be merged, as they have dissimilar ranks. Only the first one will be active.")
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return
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shared.model.add_weighted_adapter(shared.lora_names, [1] * len(shared.lora_names), "__merged")
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shared.model.set_adapter("__merged")
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