Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "ranggafermata/Fermata-v1.2-light"  # replace with your actual repo
+
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained(model_id, attn_implementation="eager")
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    device_map="auto",
+    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+    attn_implementation="eager"
+)
+model.eval()
+
+# Generation function
+def chat(prompt, max_new_tokens=256, temperature=0.8, top_p=0.95):
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=max_new_tokens,
+            temperature=temperature,
+            top_p=top_p,
+            do_sample=True,
+            pad_token_id=tokenizer.eos_token_id,
+        )
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# Gradio interface
+gr.Interface(
+    fn=chat,
+    inputs=[
+        gr.Textbox(lines=4, label="Prompt"),
+        gr.Slider(64, 1024, value=256, step=64, label="Max New Tokens"),
+        gr.Slider(0.1, 1.5, value=0.8, step=0.1, label="Temperature"),
+        gr.Slider(0.1, 1.0, value=0.95, step=0.05, label="Top-p")
+    ],
+    outputs=gr.Textbox(label="Response"),
+    title="Fermata Assistant (Gemma 3 - 1B - IT)",
+    description="A smart assistant built on Gemma 3B with personality from the Fermata project."
+).launch()