diff --git a/peft/full_model.py b/peft/full_model.py
new file mode 100644
index 0000000..36edbcf
--- /dev/null
+++ b/peft/full_model.py
@@ -0,0 +1,165 @@
+import numpy as np
+import torch
+from datasets import load_dataset
+from sklearn.metrics import precision_recall_fscore_support, precision_recall_curve
+from transformers import (
+    AutoTokenizer,
+    AutoModelForSequenceClassification,
+    Trainer,
+    TrainingArguments,
+    set_seed
+)
+from peft import get_peft_model, LoraConfig, TaskType
+
+# Set seed for reproducibility
+set_seed(42)
+
+# Load dataset
+ds = load_dataset("TUKE-KEMT/hate_speech_slovak")
+train_dataset = ds['train']
+train_test_split = ds['train'].train_test_split(test_size=0.2, seed=42)
+val_dataset = train_test_split['test']
+train_dataset = train_test_split['train']
+test_dataset = ds['test']
+
+# Load tokenizer and base model
+model_name = "ApoTro/slovak-t5-small"
+tokenizer = AutoTokenizer.from_pretrained(model_name, force_download=True)
+model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2)
+
+# Apply LoRA tuning
+peft_config = LoraConfig(
+    task_type=TaskType.SEQ_CLS,
+    inference_mode=False,
+    r=8,
+    lora_alpha=32,
+    lora_dropout=0.1
+)
+model = get_peft_model(model, peft_config)
+
+def tokenize(batch):
+    return tokenizer(
+        batch["text"], 
+        padding="max_length", 
+        truncation=True, 
+        max_length=128
+    )
+
+def prepare_dataset(dataset):
+    dataset = dataset.map(tokenize, batched=True, remove_columns=["text"])
+    return dataset.rename_column("label", "labels")
+
+train_dataset = prepare_dataset(train_dataset)
+val_dataset = prepare_dataset(val_dataset)
+test_dataset = prepare_dataset(test_dataset)
+
+# Set device
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+# Define training arguments
+training_args = TrainingArguments(
+    output_dir="./hate_speech_model",
+    per_device_train_batch_size=8,
+    per_device_eval_batch_size=16,
+    learning_rate=3e-5,
+    num_train_epochs=7,
+    evaluation_strategy="epoch",
+    save_strategy="epoch",
+    load_best_model_at_end=True,
+    metric_for_best_model="f1",
+    greater_is_better=True,
+    warmup_steps=100,
+    weight_decay=0.01,
+    report_to="none",
+    seed=42,
+    logging_steps=10,
+    gradient_accumulation_steps=2,
+    lr_scheduler_type="cosine",
+    logging_dir='./logs',
+)
+
+def compute_metrics(pred):
+    logits = pred.predictions[0]
+    preds = logits.argmax(-1)
+    labels = pred.label_ids
+    precision, recall, f1, _ = precision_recall_fscore_support(
+        labels, preds, average='binary'
+    )
+    return {
+        'precision': precision,
+        'recall': recall,
+        'f1': f1
+    }
+
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=train_dataset,
+    eval_dataset=val_dataset,
+    compute_metrics=compute_metrics,
+)
+
+trainer.train()
+trainer.save_model("./hate_speech_model/best_model")
+
+# Reload fine-tuned model
+model = AutoModelForSequenceClassification.from_pretrained("./hate_speech_model/best_model")
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    eval_dataset=val_dataset,
+    compute_metrics=compute_metrics,
+)
+
+def find_optimal_threshold(trainer, dataset):
+    predictions = trainer.predict(dataset)
+    logits = predictions.predictions
+    
+    if isinstance(logits, tuple):
+        logits = logits[0]
+    logits = torch.tensor(logits)
+    
+    probs = torch.nn.functional.softmax(logits, dim=-1)
+    positive_probs = probs[:, 1].numpy()
+    true_labels = predictions.label_ids
+    
+    precisions, recalls, thresholds = precision_recall_curve(true_labels, positive_probs)
+    f1_scores = 2 * (precisions * recalls) / (precisions + recalls + 1e-8)
+    
+    optimal_idx = np.argmax(f1_scores[:-1])
+    optimal_threshold = thresholds[optimal_idx]
+    
+    return optimal_threshold, precisions[optimal_idx], recalls[optimal_idx], f1_scores[optimal_idx]
+
+def evaluate_with_threshold(trainer, dataset, threshold=0.5):
+    predictions = trainer.predict(dataset)
+    logits = predictions.predictions
+    
+    if isinstance(logits, tuple):
+        logits = logits[0]
+    logits = torch.tensor(logits)
+    
+    probs = torch.nn.functional.softmax(logits, dim=-1)
+    predicted_labels = (probs[:, 1] > threshold).numpy().astype(int)
+    true_labels = predictions.label_ids
+    
+    precision, recall, f1, _ = precision_recall_fscore_support(
+        true_labels, predicted_labels, average='binary', zero_division=0
+    )
+    
+    return {
+        'precision': precision,
+        'recall': recall,
+        'f1': f1
+    }
+
+print("\n🔍 Finding optimal threshold...")
+optimal_threshold, best_precision, best_recall, best_f1 = find_optimal_threshold(trainer, val_dataset)
+print(f"✅ Optimal threshold: {optimal_threshold:.4f}")
+
+print("\n📊 Evaluating on the test set with the optimal threshold:")
+optimized_results = evaluate_with_threshold(trainer, test_dataset, threshold=optimal_threshold)
+print(f"🎯 Precision: {optimized_results['precision']:.4f}")
+print(f"🎯 Recall: {optimized_results['recall']:.4f}")
+print(f"🎯 F1-score: {optimized_results['f1']:.4f}")