Updated README.md file

README.md

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 ---
 license: mit
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+
+# Query Complexity Classifier
+
+This model classifies user queries based on their **complexity level** so they can be routed to an appropriate Large Language Model (LLM).
+
+The model predicts three classes:
+
+* **Simple**
+* **Medium**
+* **Complex**
+
+It can be used as a **pre-routing layer** in AI systems where different LLMs handle different levels of query complexity.
+
+---
+
+## Model
+
+Base Model: DistilBERT
+Task: Text Classification (3 classes)
+
+---
+
+## Download and Use
+
+You can download and load the model directly from Hugging Face using the `transformers` library.
+
+### Install dependencies
+
+```bash
+pip install transformers torch
+```
+
+### Load the model
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+model_name = "Shaheer001/Query-Complexity-Classifier"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+```
+
+### Run inference
+
+```python
+text = "Explain how Kubernetes architecture works."
+
+inputs = tokenizer(text, return_tensors="pt", truncation=True)
+
+outputs = model(**inputs)
+
+prediction = torch.argmax(outputs.logits, dim=1).item()
+
+labels = ["Simple", "Medium", "Complex"]
+
+print("Predicted Complexity:", labels[prediction])
+```
+
+---
+
+## Example
+
+Input:
+
+```
+Explain Kubernetes architecture
+```
+
+Output:
+
+```
+Complex
+```
+
+---
+
+## Use Case
+
+This model can be used to build **LLM routing systems** where queries are automatically sent to different language models depending on their complexity.
+
+Example workflow:
+
+User Query → Complexity Classifier → LLM Router → Selected LLM