update address and port for spaces

ui/main.py

@@ -0,0 +1,98 @@
+"""
+This module is used to launch Axolotl with user defined configurations.
+"""
+
+import gradio as gr
+import yaml
+
+
+def config(
+    base_model,
+    dataset,
+    dataset_type,
+    learn_rate,
+    gradient_accumulation_steps,
+    micro_batch_size,
+    seq_length,
+    num_epochs,
+    output_dir,
+    val_size,
+):
+    """
+    This function generates a configuration dictionary and saves it as a yaml file.
+    """
+    config_dict = {
+        "base_model": base_model,
+        "datasets": [{"path": dataset, "type": dataset_type}],
+        "learning_rate": learn_rate,
+        "gradient_accumulation_steps": gradient_accumulation_steps,
+        "micro_batch_size": micro_batch_size,
+        "sequence_len": seq_length,
+        "num_epochs": num_epochs,
+        "output_dir": output_dir,
+        "val_set_size": val_size,
+    }
+    with open("config.yml", "w", encoding="utf-8") as file:
+        yaml.dump(config_dict, file)
+    print(config_dict)
+    return yaml.dump(config_dict)
+
+
+with gr.Blocks(title="Axolotl Launcher") as demo:
+    gr.Markdown(
+        """
+    # Axolotl Launcher
+    Fill out the required fields below to create a training run.
+    """
+    )
+    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.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")
+
+        output_dir_path = gr.Textbox("./model-out", label="Output directory")
+
+    create_config = gr.Button("Create config")
+    output = gr.TextArea(label="Generated config")
+    create_config.click(
+        config,
+        inputs=[
+            base_model_name,
+            dataset_path,
+            dataset_type_name,
+            learning_rate,
+            gradient_accumulation_steps_count,
+            micro_batch_size_count,
+            sequence_length,
+            num_epochs_count,
+            output_dir_path,
+            val_set_size_count,
+        ],
+        outputs=output,
+    )
+
+demo.launch(debug=True, server_name="0.0.0.0", server_port=7860)