Create app.py

app.py

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+import torch
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+MODEL_ID = "google/gemma-3-1b-it"
+
+SYSTEM_PROMPT = """You are THMEXAAI, a helpful AI assistant.
+
+Your name is THMEXAAI, powered by a SLM (Small Language Model).
+
+You were pretrained on 140+ languages (including low-resource languages).
+
+Out-of-the-box, you can communicate in 35+ languages and are optimized for instruction following.
+
+Supported languages include (but are not limited to):
+English, Spanish, French, German, Italian, Portuguese, Dutch, Swedish, Norwegian, Danish, Finnish, Polish, Czech, Slovak, Hungarian, Romanian, Bulgarian, Greek, Turkish, Arabic, Hebrew, Russian, Ukrainian, Hindi, Bengali, Urdu, Tamil, Telugu, Indonesian, Malay, Vietnamese, Thai, Chinese, Japanese and Korean.
+
+Always:
+- Be helpful
+- Be accurate
+- Follow user instructions
+- Answer in the user's language whenever possible
+- Explain clearly
+- Be concise unless more detail is requested
+
+You are THMEXAAI.
+"""
+
+print("Loading tokenizer...")
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+
+print("Loading model...")
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_ID,
+    torch_dtype=torch.float32,
+    device_map="cpu",
+    low_cpu_mem_usage=True,
+)
+
+model.eval()
+
+def chat(message, history):
+    messages = [
+        {
+            "role": "system",
+            "content": SYSTEM_PROMPT,
+        }
+    ]
+
+    for user_msg, assistant_msg in history:
+        messages.append(
+            {
+                "role": "user",
+                "content": user_msg,
+            }
+        )
+        messages.append(
+            {
+                "role": "assistant",
+                "content": assistant_msg,
+            }
+        )
+
+    messages.append(
+        {
+            "role": "user",
+            "content": message,
+        }
+    )
+
+    inputs = tokenizer.apply_chat_template(
+        messages,
+        tokenize=True,
+        add_generation_prompt=True,
+        return_tensors="pt",
+    )
+
+    with torch.no_grad():
+        outputs = model.generate(
+            inputs,
+            max_new_tokens=512,
+            do_sample=True,
+            temperature=0.7,
+            top_p=0.95,
+            repetition_penalty=1.05,
+            pad_token_id=tokenizer.eos_token_id,
+            eos_token_id=tokenizer.eos_token_id,
+        )
+
+    generated = outputs[0][inputs.shape[-1]:]
+
+    response = tokenizer.decode(
+        generated,
+        skip_special_tokens=True,
+    )
+
+    return response
+
+demo = gr.ChatInterface(
+    fn=chat,
+    title="THMEXAAI",
+    description="Powered by Gemma 3 1B IT",
+    examples=[
+        "Hello!",
+        "Who are you?",
+        "Explain machine learning",
+        "Hola, ¿puedes hablar español?",
+        "日本語で自己紹介してください"
+    ],
+)
+
+if __name__ == "__main__":
+    demo.launch()