LlaMA3.2 testing interface with Gradio

app.py

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+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load LLaMA 3.2 model and tokenizer
+model_name = "meta-llama/LLaMA-3.2"  # Replace this with the correct model ID if necessary
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Define the inference function
+def generate_text(prompt, max_length=100, temperature=0.7):
+    inputs = tokenizer(prompt, return_tensors="pt")
+    output = model.generate(inputs['input_ids'], max_length=max_length, temperature=temperature)
+    return tokenizer.decode(output[0], skip_special_tokens=True)
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=generate_text,
+    inputs=[
+        gr.inputs.Textbox(label="Enter your prompt", placeholder="Start typing..."),
+        gr.inputs.Slider(minimum=50, maximum=200, default=100, label="Max Length"),
+        gr.inputs.Slider(minimum=0.1, maximum=1.0, default=0.7, label="Temperature"),
+    ],
+    outputs="text",
+    title="LLaMA 3.2 Text Generator",
+    description="Enter a prompt to generate text using the LLaMA 3.2 model.",
+    theme="compact",
+)
+
+# Launch the Gradio app
+iface.launch()