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app.py

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+import gradio as gr
+import torch
+from transformers import pipeline
+
+MODEL_ID = "beta3/gemma3_1b_title_generator"
+
+# Cargar pipeline (esto descarga el modelo base + adapter)
+pipe = pipeline(
+    "text-generation",
+    model=MODEL_ID,
+    dtype=torch.bfloat16,
+    device_map="auto"
+)
+
+def generate_title(abstract, temperature=0.7, top_p=0.9, max_tokens=32):
+    if not abstract.strip():
+        return "Please provide an abstract."
+
+    prompt = f"""<bos><start_of_turn>user
+Generate a simple title for the following abstract:
+{abstract}
+<end_of_turn>
+<start_of_turn>model
+"""
+
+    output = pipe(
+        prompt,
+        max_new_tokens=max_tokens,
+        do_sample=True,
+        temperature=temperature,
+        top_p=top_p,
+        return_full_text=False
+    )
+
+    return output[0]["generated_text"].strip()
+
+
+with gr.Blocks(title="Gemma 3 Title Generator") as demo:
+    gr.Markdown("## 📄 Academic Title Generator (Gemma 3 1B Fine-tuned)")
+
+    abstract_input = gr.Textbox(
+        lines=8,
+        label="Abstract",
+        placeholder="Paste your academic abstract here..."
+    )
+
+    with gr.Row():
+        temperature = gr.Slider(0.1, 1.5, value=0.7, label="Temperature")
+        top_p = gr.Slider(0.1, 1.0, value=0.9, label="Top-p")
+        max_tokens = gr.Slider(8, 64, value=32, step=1, label="Max new tokens")
+
+    generate_button = gr.Button("Generate Title 🚀")
+    output_title = gr.Textbox(label="Generated Title")
+
+    generate_button.click(
+        generate_title,
+        inputs=[abstract_input, temperature, top_p, max_tokens],
+        outputs=output_title
+    )
+
+demo.launch()

requirements.txt

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+torch
+transformers>=4.48.0
+accelerate
+peft
+safetensors
+gradio
+bitsandbytes
+unsloth