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[model] unsloth resume from checkpoint bug (#8423)
Co-authored-by: viyer <vivek_iyer2@apple.com>
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@ -188,7 +188,7 @@ def _setup_lora_tuning(
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if adapter_to_resume is not None: # resume lora training
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if adapter_to_resume is not None: # resume lora training
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if model_args.use_unsloth:
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if model_args.use_unsloth:
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model = load_unsloth_peft_model(config, model_args, is_trainable=is_trainable)
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model = load_unsloth_peft_model(config, model_args, finetuning_args, is_trainable=is_trainable)
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else:
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else:
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model = PeftModel.from_pretrained(model, adapter_to_resume, is_trainable=is_trainable, **init_kwargs)
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model = PeftModel.from_pretrained(model, adapter_to_resume, is_trainable=is_trainable, **init_kwargs)
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@ -80,12 +80,12 @@ def get_unsloth_peft_model(
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def load_unsloth_peft_model(
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def load_unsloth_peft_model(
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config: "PretrainedConfig", model_args: "ModelArguments", is_trainable: bool
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config: "PretrainedConfig", model_args: "ModelArguments", finetuning_args: "FinetuningArguments", is_trainable: bool
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) -> "PreTrainedModel":
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) -> "PreTrainedModel":
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r"""Load peft model with unsloth. Used in both training and inference."""
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r"""Load peft model with unsloth. Used in both training and inference."""
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from unsloth import FastLanguageModel # type: ignore
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from unsloth import FastLanguageModel # type: ignore
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unsloth_kwargs = _get_unsloth_kwargs(config, model_args.adapter_name_or_path[0], model_args)
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unsloth_kwargs = _get_unsloth_kwargs(config, model_args.adapter_name_or_path[0], model_args, finetuning_args)
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try:
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try:
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if not is_trainable:
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if not is_trainable:
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unsloth_kwargs["use_gradient_checkpointing"] = False
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unsloth_kwargs["use_gradient_checkpointing"] = False
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