added training set and path in code

app.py

@@ -8,6 +8,7 @@ For more information on `huggingface_hub` Inference API support, please check th
 """
 client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
+
 def respond(
     message,
     history: list[tuple[str, str]],
@@ -40,9 +41,11 @@ def respond(
         response += token
         yield response
 
+
 def train_model():
     # Load dataset
-    dataset = load_dataset('your_dataset_name')
+    dataset = load_dataset('json', data_files={
+                           'train': 'path/to/training_set.json'})
 
     # Load model
     model = AutoModelForCausalLM.from_pretrained('meta-llama/Meta-Llama-3-8B')
@@ -68,6 +71,7 @@ def train_model():
     trainer.train()
     return "Training complete"
 
+
 """
 For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
 """
@@ -79,9 +83,12 @@ with 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.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,

training_set.json

@@ -0,0 +1,42 @@
+{
+  "train": [
+    {
+      "messages": [
+        { "role": "system", "content": "You are a helpful assistant." },
+        { "role": "user", "content": "How do I train a Llama 3 model?" },
+        {
+          "role": "assistant",
+          "content": "To train a Llama 3 model, you need to load the dataset, define training arguments, initialize the Trainer, and start the training process. Make sure to use appropriate libraries such as Hugging Face Transformers and datasets."
+        }
+      ]
+    },
+    {
+      "messages": [
+        { "role": "system", "content": "You are a helpful assistant." },
+        {
+          "role": "user",
+          "content": "Can you generate text using the Llama 3 model?"
+        },
+        {
+          "role": "assistant",
+          "content": "Yes, you can generate text using the Llama 3 model by tokenizing your input text, feeding it to the model, and decoding the output. The Hugging Face Transformers library provides tools to facilitate this process."
+        }
+      ]
+    }
+  ],
+  "test": [
+    {
+      "messages": [
+        { "role": "system", "content": "You are a helpful assistant." },
+        {
+          "role": "user",
+          "content": "What are the main features of the Llama 3 model?"
+        },
+        {
+          "role": "assistant",
+          "content": "The main features of the Llama 3 model include its ability to generate coherent and contextually relevant responses in conversational settings, support for large-scale language understanding tasks, and compatibility with various fine-tuning and deployment tools provided by the Hugging Face ecosystem."
+        }
+      ]
+    }
+  ]
+}