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app.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+from transformers import AutoTokenizer, LlamaForCausalLM
+import torch
+import os
+
+app = FastAPI(title="My 500M AI API")
+
+# Pointing to the Model you built!
+REPO_NAME = "Sdey10/My-500M-Mini-TUF"
+
+print("Downloading Model from Hugging Face...")
+# We fetch the public model without hardcoding your secret token
+tokenizer = AutoTokenizer.from_pretrained(REPO_NAME)
+model = LlamaForCausalLM.from_pretrained(REPO_NAME)
+
+# Free Hugging Face Spaces run on CPUs
+model.to("cpu")
+model.eval()
+
+class PromptRequest(BaseModel):
+    prompt: str
+    max_tokens: int = 50
+
+@app.post("/generate")
+def generate_text(request: PromptRequest):
+    inputs = tokenizer(request.prompt, return_tensors="pt").to("cpu")
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs, 
+            max_new_tokens=request.max_tokens,
+            temperature=0.7,
+            do_sample=True,
+            repetition_penalty=1.2
+        )
+    
+    response_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return {"response": response_text}