Update README.md

README.md

@@ -49,6 +49,30 @@ a compact Mixture-of-Experts (MoE) model
 
 ---
 
+## Usage
+To load the fine-tuned model:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "sandlogic/indicphi-mini-moe-v3"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    device_map="auto",
+    load_in_4bit=True
+)
+
+prompt = "ग्रामीण क्षेत्रों में ऑनलाइन शिक्षा की समस्याएं क्या हैं?"  
+
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+outputs = model.generate(**inputs, max_new_tokens=100)
+
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+
+```
+
 ##  Dataset Preparation
 ### Data Sources
 - **Total collected:** 561M samples from **53 datasets** from Hugging Face.  
@@ -151,29 +175,7 @@ Accuracy: **(Phi-mini-MoE) 21.03 → (IndicPhi-mini) 24.46 (+3.43%)**
 
 Accuracy: **(Phi-mini-MoE) 27.47 → (IndicPhi-mini) 30.95 (+3.48%)**
 
-## Usage
-To load the fine-tuned model:
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-model_name = "sandlogic/indicphi-mini-moe-v3"
 
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(
-    model_name,
-    device_map="auto",
-    load_in_4bit=True
-)
-
-prompt = "ग्रामीण क्षेत्रों में ऑनलाइन शिक्षा की समस्याएं क्या हैं?"  
-
-inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
-outputs = model.generate(**inputs, max_new_tokens=100)
-
-print(tokenizer.decode(outputs[0], skip_special_tokens=True))
-
-```
 
 ## Acknowledgments