create config

ui/main.py

@@ -1,7 +1,6 @@
 """
 This module is used to launch Axolotl with user defined configurations.
 """
-import subprocess
 
 import gradio as gr
 import yaml
@@ -39,23 +38,6 @@ def config(
     return yaml.dump(config_dict)
 
 
-def create_training_job():
-    # Start a long-running process
-    process = subprocess.Popen(
-        ["accelerate launch -m axolotl.cli.train config.yml"],
-        stdout=subprocess.PIPE,
-        stderr=subprocess.STDOUT,
-        text=True,
-    )
-
-    # Read the output line by line as it becomes available
-    while True:
-        line = process.stdout.readline()
-        if not line:
-            break  # No more output
-        print(line.strip())
-
-
 with gr.Blocks(title="Axolotl Launcher") as demo:
     gr.Markdown(
         """
@@ -63,34 +45,36 @@ with gr.Blocks(title="Axolotl Launcher") as demo:
     Fill out the required fields below to create a training run.
     """
     )
-    base_model_name = gr.Textbox(
-        "TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T", label="Base model"
-    )
+    with gr.Row():
+        base_model_name = gr.Textbox(
+            "TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T", label="Base model"
+        )
+
+        mode = gr.Radio(
+            choices=["Full finetune", "QLoRA", "LoRA"],
+            label="Training mode",
+            info="FFT = 16 bit, Qlora = 4 bit, Lora = 8 bit",
+        )
     with gr.Row():
         dataset_path = gr.Textbox("mhenrichsen/alpaca_2k_test", label="Dataset")
         dataset_type_name = gr.Dropdown(
             choices=["alpaca", "sharegpt"], label="Dataset type", value="alpaca"
         )
-    with gr.Row():
-        learning_rate = gr.Number(0.000001, label="Learning rate")
-        gradient_accumulation_steps_count = gr.Number(
-            1, label="Gradient accumulation steps"
-        )
-        val_set_size_count = gr.Number(0, label="Validation size")
+    with gr.Accordion("Hyperparameters", open=False):
+        gr.Markdown("Choose hyperparameters")
+        with gr.Row():
+            learning_rate = gr.Number(0.000001, label="Learning rate")
+            gradient_accumulation_steps_count = gr.Number(
+                1, label="Gradient accumulation steps"
+            )
+            val_set_size_count = gr.Number(0, label="Validation size")
 
-    with gr.Row():
-        micro_batch_size_count = gr.Number(1, label="Micro batch size")
-        sequence_length = gr.Number(1024, label="Sequence length")
-        num_epochs_count = gr.Number(1, label="Epochs")
+        with gr.Row():
+            micro_batch_size_count = gr.Number(1, label="Micro batch size")
+            sequence_length = gr.Number(1024, label="Sequence length")
+            num_epochs_count = gr.Number(1, label="Epochs")
 
-    output_dir_path = gr.Textbox("./model-out", label="Output directory")
-
-    mode = gr.Radio(
-        choices=["Full finetune", "QLoRA", "LoRA"],
-        value="Full finetune",
-        label="Training mode",
-        info="FFT = 16 bit, Qlora = 4 bit, Lora = 8 bit",
-    )
+        output_dir_path = gr.Textbox("./model-out", label="Output directory")
 
     create_config = gr.Button("Create config")
     output = gr.TextArea(label="Generated config")
@@ -111,7 +95,4 @@ with gr.Blocks(title="Axolotl Launcher") as demo:
         outputs=output,
     )
 
-    start_training = gr.Button("Start training")
-    start_training.click(create_training_job)
-
-demo.launch(share=True)
+demo.launch()