obuch with cross and graphic SAVING LORA

.ipynb_checkpoints/checkLora-checkpoint.py

-from peft import PeftConfig
+from transformers import BertModel, BertTokenizer
+from peft import PeftModel
+import torch
 
-config = PeftConfig.from_pretrained("./fine-tuned-bert-lora")
-print("Базовая модель:", config.base_model_name_or_path)
-print("LoRA config:", config)
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+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Загружаем базовую модель
+base_model = BertModel.from_pretrained("bert-base-uncased").to(device)
+
+# Загружаем адаптер LoRA
+model = PeftModel.from_pretrained(base_model, "./fine-tuned-bert-lora_new").to(device)
+
+# Загружаем токенизатор
+tokenizer = BertTokenizer.from_pretrained("./fine-tuned-bert-lora_new")
+
+# Пример инференса
+text = "This is a test prompt."
+inputs = tokenizer(text, return_tensors="pt").to(device)
+with torch.no_grad():
+    outputs = model(**inputs)
+
+print("LoRA адаптер загружен, выходы модели:", outputs)
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checkLora.py

-from peft import PeftConfig
+from transformers import BertModel, BertTokenizer
+from peft import PeftModel
+import torch
 
-config = PeftConfig.from_pretrained("./fine-tuned-bert-lora")
-print("Базовая модель:", config.base_model_name_or_path)
-print("LoRA config:", config)
\ No newline at end of file
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Загружаем базовую модель
+base_model = BertModel.from_pretrained("bert-base-uncased").to(device)
+
+# Загружаем адаптер LoRA
+model = PeftModel.from_pretrained(base_model, "./fine-tuned-bert-lora_new").to(device)
+
+# Загружаем токенизатор
+tokenizer = BertTokenizer.from_pretrained("./fine-tuned-bert-lora_new")
+
+# Пример инференса
+text = "This is a test prompt."
+inputs = tokenizer(text, return_tensors="pt").to(device)
+with torch.no_grad():
+    outputs = model(**inputs)
+
+print("LoRA адаптер загружен, выходы модели:", outputs)
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