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+# DistilBERT with LoRA for Intent Recognition
+
+This is a parameter-efficient fine-tuned version of `distilbert-base-uncased` using the LoRA technique via [PEFT](https://github.com/huggingface/peft). The model was trained for intent recognition using a custom dataset.
+
+## 🧾 Model Details
+- Base model: `distilbert-base-uncased`
+- Fine-tuning method: [LoRA (Low-Rank Adaptation)](https://arxiv.org/abs/2106.09685) using 🤗 PEFT
+- Task: Intent Recognition (Text Classification)
+
+## 🧠 Intended Use
+You can use this model to classify user intents in applications like chatbots, virtual assistants, or voice-based interfaces.
+
+## 📥 How to Use
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from peft import PeftModel
+
+# Load tokenizer and base model
+tokenizer = AutoTokenizer.from_pretrained("hopjetair/intent")
+base_model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased",num_labels=33)
+
+# Load LoRA adapter
+model = PeftModel.from_pretrained(base_model, "hopjetair/intent")
+
+# Inference
+text = "Book me a flight to New York"
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model(**inputs)
+intent_class = outputs.logits.argmax(dim=1).item()