Add training functionality to Gradio app

app.py

@@ -1,12 +1,13 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
+from transformers import AutoModelForCausalLM, Trainer, TrainingArguments
+from datasets import load_dataset
 
 """
 For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
 """
 client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
-
 def respond(
     message,
     history: list[tuple[str, str]],
@@ -39,25 +40,62 @@ def respond(
         response += token
         yield response
 
+def train_model():
+    # Load dataset
+    dataset = load_dataset('your_dataset_name')
+
+    # Load model
+    model = AutoModelForCausalLM.from_pretrained('meta-llama/Meta-Llama-3-8B')
+
+    # Define training arguments
+    training_args = TrainingArguments(
+        output_dir='./results',
+        num_train_epochs=3,
+        per_device_train_batch_size=16,
+        save_steps=10_000,
+        save_total_limit=2,
+    )
+
+    # Initialize Trainer
+    trainer = Trainer(
+        model=model,
+        args=training_args,
+        train_dataset=dataset['train'],
+        eval_dataset=dataset['test']
+    )
+
+    # Start training
+    trainer.train()
+    return "Training complete"
+
 """
 For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
 """
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
+demo = gr.Blocks()
+
+with demo:
+    gr.Markdown("# Llama3training Chatbot and Model Trainer")
+    with gr.Tab("Chat"):
+        gr.ChatInterface(
+            respond,
+            additional_inputs=[
+                gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
+                gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
+                gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
+                gr.Slider(
+                    minimum=0.1,
+                    maximum=1.0,
+                    value=0.95,
+                    step=0.05,
+                    label="Top-p (nucleus sampling)",
+                ),
+            ],
+        )
+    with gr.Tab("Train"):
+        train_button = gr.Button("Start Training")
+        train_output = gr.Textbox(label="Training Output")
 
+        train_button.click(train_model, outputs=train_output)
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()

workspace.code-workspace

@@ -0,0 +1,8 @@
+{
+	"folders": [
+		{
+			"path": "."
+		}
+	],
+	"settings": {}
+}