Update README.md

README.md

@@ -17,13 +17,13 @@ tags: []
 
 This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
+- **Developed by:** Sandhanapandiyan
+- **Funded by [optional]:** 
 - **Shared by [optional]:** [More Information Needed]
 - **Model type:** [More Information Needed]
 - **Language(s) (NLP):** [More Information Needed]
 - **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+- **Finetuned from model [optional]:** microsoft/phi
 
 ### Model Sources [optional]
 
@@ -39,7 +39,42 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# Load the model and tokenizer
+model_path = "/content/drive/MyDrive/sandhanapandiyan/Responce Generator"
+tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto", torch_dtype=torch.float16)
+
+# Generate a response
+def generate_response(user_query, sql_result, max_tokens=150):
+    prompt = f"User: {user_query}\nSQL Result: {sql_result}\nAssistant:"
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=max_tokens,
+            do_sample=True,
+            temperature=0.7,
+            top_p=0.9,
+            eos_token_id=tokenizer.eos_token_id,
+        )
+    
+    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    
+    # Extract only the assistant's response
+    return generated_text.split("Assistant:")[-1].strip()
+
+# Example usage
+user_query = "list all the employee"
+sql_result = "Emily Watson"
+response = generate_response(user_query, sql_result)
+
+print("🔍 Generated Response:")
+print(response)
+
 
 [More Information Needed]