diff --git a/peft/model_few_shot_peft.py b/peft/model_few_shot_peft.py
new file mode 100644
index 0000000..ae228ed
--- /dev/null
+++ b/peft/model_few_shot_peft.py
@@ -0,0 +1,140 @@
+import numpy as np
+import torch
+from datasets import load_dataset, concatenate_datasets
+from sklearn.metrics import precision_recall_fscore_support
+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")
+label_0 = ds['train'].filter(lambda example: example['label'] == 0)
+label_1 = ds['train'].filter(lambda example: example['label'] == 1)
+
+def create_stratified_split(label_0, label_1, n_samples, seed=42):
+    few_shot_0 = label_0.shuffle(seed=seed).select(range(n_samples))
+    few_shot_1 = label_1.shuffle(seed=seed).select(range(n_samples))
+    return concatenate_datasets([few_shot_0, few_shot_1]).shuffle(seed=seed)
+
+train_dataset = create_stratified_split(label_0, label_1, n_samples=40)
+val_dataset = create_stratified_split(label_0, label_1, n_samples=10, seed=43)
+test_dataset = create_stratified_split(label_0, label_1, n_samples=50, seed=44)
+
+# 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")
+
+# Evaluate model
+results = trainer.evaluate(test_dataset)
+print("\n📊 Evaluation results on test set:")
+print(f"🎯 Precision: {results['eval_precision']:.4f}")
+print(f"🎯 Recall: {results['eval_recall']:.4f}")
+print(f"🎯 F1-score: {results['eval_f1']:.4f}")
+
+# Function for text classification
+#def classify_text(text, model, tokenizer, device):
+#    model.eval()
+#    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding="max_length", max_length=128).to(device)
+#    with torch.no_grad():
+#        outputs = model(**inputs)
+#    logits = outputs.logits
+#    pred = torch.argmax(logits, dim=-1).item()
+#    return "🛑 Hate Speech" if pred == 1 else "✅ Not Hate Speech"
+
+# Testing examples
+test_texts = [
+    "Toto je úplne normálny text bez nenávisti.",
+    "Nenávidím ťa a všetkých ako ty!",
+    "Každý má právo na svoj názor, ale musíme byť rešpektujúci.",
+    "Všetci ľudia tejto skupiny sú strašní a mali by byť vyhodení!"
+]
+
+print("\n🔍 Testing custom inputs:")
+for text in test_texts:
+    result = classify_text(text, model, tokenizer, device)
+    print(f"📝 Text: {text}\n➡️ Prediction: {result}\n")