Create use.py

use.py

@@ -0,0 +1,46 @@
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+def run_pin_inference(prompt, model_id="LH-Tech-AI/Pin-Tiny", subfolder="Pin-25M"):
+    # 1. Device Setup
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    print(f"Using device: {device}")
+
+    # 2. Load tokenizer and model
+    tokenizer = AutoTokenizer.from_pretrained("gpt2")
+    tokenizer.pad_token = tokenizer.eos_token
+    
+    model = AutoModelForCausalLM.from_pretrained(model_id, subfolder=subfolder).to(device)
+
+    # 3. Format prompt
+    formatted_prompt = f"[INST] {prompt} [/INST]"
+    inputs = tokenizer(formatted_prompt, return_tensors="pt").to(device)
+
+    # 4. Generate
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=64,
+            temperature=0.7,
+            do_sample=True,
+            pad_token_id=tokenizer.eos_token_id,
+            eos_token_id=tokenizer.encode("[")[0] 
+        )
+
+    # 5. Decode & Cleanup
+    full_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    
+    if "[/INST]" in full_text:
+        response = full_text.split("[/INST]")[-1].split("[INST]")[0].strip()
+    else:
+        response = full_text
+        
+    return response
+
+# --- Sample test ---
+if __name__ == "__main__":
+    user_query = "What is the weather like today?"
+    answer = run_pin_inference(user_query, model_id="LH-Tech-AI/Pin", subfolder="Pin-25M")
+    
+    print(f"\nUser: {user_query}")
+    print(f"Pin: {answer}")