Don't import PEFT unless necessary

modules/training.py

@@ -18,14 +18,6 @@ import gradio as gr
 import torch
 import transformers
 from datasets import Dataset, load_dataset
-from peft import (
-    LoraConfig,
-    get_peft_model,
-    prepare_model_for_kbit_training,
-    set_peft_model_state_dict
-)
-from peft.utils.other import \
-    TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING as model_to_lora_modules
 from transformers import is_torch_xpu_available
 from transformers.models.auto.modeling_auto import (
     MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
@@ -292,6 +284,16 @@ def calc_trainable_parameters(model):
 
 def do_train(lora_name: str, always_override: bool, q_proj_en: bool, v_proj_en: bool, k_proj_en: bool, o_proj_en: bool, gate_proj_en: bool, down_proj_en: bool, up_proj_en: bool, save_steps: int, micro_batch_size: int, batch_size: int, epochs: int, learning_rate: str, lr_scheduler_type: str, lora_rank: int, lora_alpha: int, lora_dropout: float, cutoff_len: int, dataset: str, eval_dataset: str, format: str, eval_steps: int, raw_text_file: str, overlap_len: int, newline_favor_len: int, higher_rank_limit: bool, warmup_steps: int, optimizer: str, hard_cut_string: str, train_only_after: str, stop_at_loss: float, add_eos_token: bool, min_chars: int, report_to: str):
 
+    from peft import (
+        LoraConfig,
+        get_peft_model,
+        prepare_model_for_kbit_training,
+        set_peft_model_state_dict
+    )
+    from peft.utils.other import \
+        TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING as \
+        model_to_lora_modules
+
     global WANT_INTERRUPT
     WANT_INTERRUPT = False