Update app.py

app.py

@@ -1,367 +1,129 @@
-"""
-This module is used to launch Axolotl with user defined configurations.
-"""
-
 import gradio as gr
-import yaml
-from config import config
-
-example_yml = """
-base_model: NousResearch/Llama-2-7b-hf
-model_type: LlamaForCausalLM
-tokenizer_type: LlamaTokenizer
-
-load_in_8bit: false
-load_in_4bit: true
-strict: false
-
-datasets:
-  - path: mhenrichsen/alpaca_2k_test
-    type: alpaca
-dataset_prepared_path:
-val_set_size: 0.05
-output_dir: ./qlora-out
-
-adapter: qlora
-lora_model_dir:
-
-sequence_len: 4096
-sample_packing: true
-pad_to_sequence_len: true
-
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_modules:
-lora_target_linear: true
-lora_fan_in_fan_out:
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 2
-num_epochs: 4
-optimizer: paged_adamw_32bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: auto
-fp16:
-tf32: false
-
-gradient_checkpointing: true
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention: true
-
-warmup_steps: 10
-evals_per_epoch: 4
-eval_table_size:
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:
-"""
-
-
-def yml_config(yml_config):
-  """
-    This function saves as a yaml file from user text.
-    """
-  yml_config = yaml.safe_load(yml_config)
-  with open("config.yml", "w", encoding="utf-8") as file:
-    yaml.dump(yml_config, file)
-  # print(yml_config)
-  return yaml.dump(yml_config)
-
-
-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.Tab("Base Model & Tokenizer"):
-    with gr.Column():
-      with gr.Row():
-        base_model = gr.Textbox(label="Base Model")
-        base_model_ignore_patterns = gr.Textbox(
-            label="Base Model Ignore Patterns")
-        base_model_config = gr.Textbox(label="Base Model Config")
-        model_revision = gr.Textbox(label="Model Revision")
-      with gr.Row():
-        tokenizer_config = gr.Textbox(label="Tokenizer Config")
-        model_type = gr.Textbox(label="Model Type")
-        tokenizer_type = gr.Textbox(label="Tokenizer Type")
-      with gr.Row():
-        trust_remote_code = gr.Checkbox(label="Trust Remote Code", value=False)
-        tokenizer_use_fast = gr.Checkbox(label="Use Fast Tokenizer",
-                                         value=True)
-        tokenizer_legacy = gr.Checkbox(label="Use Legacy Tokenizer",
-                                       value=False)
-        resize_token_embeddings_to_32x = gr.Checkbox(
-            label="Resize Token Embeddings to 32x", value=False)
-    with gr.Accordion("Adv. Config", open=False):
-      with gr.Accordion("Model Derivation & Configuration Overrides", open=False):
-        with gr.Column():
-          is_falcon_derived_model = gr.Checkbox(
-              label="Is Falcon Derived Model", value=False)
-          is_llama_derived_model = gr.Checkbox(label="Is Llama Derived Model",
-                                               value=False)
-          is_mistral_derived_model = gr.Checkbox(
-              label="Is Mistral Derived Model", value=False)
-          is_qwen_derived_model = gr.Checkbox(label="Is Qwen Derived Model",
-                                              value=False)
-          model_config = gr.TextArea(label="Model Config Overrides",
-                                     placeholder="YAML or JSON format")
-          bnb_config_kwargs = gr.TextArea(label="BnB Config KWArgs",
-                                          placeholder="YAML or JSON format")
-
-      with gr.Accordion("Quantization & Precision", open=False):
-        with gr.Column():
-          with gr.Row():
-            gptq = gr.Checkbox(label="GPTQ", value=False)
-            gptq_groupsize = gr.Number(label="GPTQ Groupsize", value=128)
-            gptq_model_v1 = gr.Checkbox(label="GPTQ Model V1", value=False)
-            load_in_8bit = gr.Checkbox(label="Load in 8-bit", value=False)
-            load_in_4bit = gr.Checkbox(label="Load in 4-bit", value=False)
-          with gr.Row():
-            bf16 = gr.Checkbox(label="BF16", value=False)
-            fp16 = gr.Checkbox(label="FP16", value=False)
-            tf32 = gr.Checkbox(label="TF32", value=False)
-            bfloat16 = gr.Checkbox(label="BFloat16", value=False)
-            float16 = gr.Checkbox(label="Float16", value=False)
-
-      with gr.Accordion("GPU & LoRA Settings", open=False):
-        gpu_memory_limit = gr.Textbox(label="GPU Memory Limit")
-        lora_on_cpu = gr.Checkbox(label="LoRA on CPU", value=False)
-        datasets = gr.TextArea(label="Datasets",
-                               placeholder="YAML or JSON format for datasets")
-        test_datasets = gr.TextArea(
-            label="Test Datasets",
-            placeholder="YAML or JSON format for test datasets")
-        rl = gr.Textbox(label="RL")
-        chat_template = gr.Textbox(label="Chat Template")
-        default_system_message = gr.Textbox(label="Default System Message")
-        dataset_prepared_path = gr.Textbox(label="Dataset Prepared Path")
-        push_dataset_to_hub = gr.Textbox(label="Push Dataset to Hub")
-        dataset_processes = gr.Number(label="Dataset Processes", value=1)
-        dataset_keep_in_memory = gr.Checkbox(label="Dataset Keep in Memory",
-                                             value=False)
-        with gr.Row():
-          hub_model_id = gr.Textbox(label="Hub Model ID")
-          hub_strategy = gr.Textbox(label="Hub Strategy")
-          hf_use_auth_token = gr.Checkbox(label="HF Use Auth Token",
-                                          value=False)
-        with gr.Row():
-          val_set_size = gr.Number(label="Validation Set Size",
-                                   value=0.04,
-                                   step=0.01)
-          dataset_shard_num = gr.Number(label="Dataset Shard Num")
-          dataset_shard_idx = gr.Number(label="Dataset Shard Index")
-
-      with gr.Accordion("Training & Evaluation", open=False):
-        with gr.Row():
-          sequence_len = gr.Number(label="Sequence Length", value=2048)
-          pad_to_sequence_len = gr.Checkbox(label="Pad to Sequence Length",
-                                            value=False)
-        with gr.Row():
-          sample_packing = gr.Checkbox(label="Sample Packing", value=False)
-          eval_sample_packing = gr.Checkbox(label="Eval Sample Packing",
-                                            value=False)
-          sample_packing_eff_est = gr.Number(label="Sample Packing Eff Est")
-        with gr.Row():
-          total_num_tokens = gr.Number(label="Total Num Tokens")
-          device_map = gr.Textbox(label="Device Map")
-          max_memory = gr.Textbox(label="Max Memory")
-          adapter = gr.Textbox(label="Adapter")
-        with gr.Column():
-          lora_model_dir = gr.Textbox(label="LoRA Model Dir")
-          lora_r = gr.Number(label="LoRA R", value=8)
-          lora_alpha = gr.Number(label="LoRA Alpha", value=16)
-          lora_dropout = gr.Number(label="LoRA Dropout", value=0.05, step=0.01)
-          lora_target_modules = gr.TextArea(label="LoRA Target Modules")
-          lora_target_linear = gr.Checkbox(label="LoRA Target Linear",
-                                           value=False)
-          lora_modules_to_save = gr.TextArea(label="LoRA Modules to Save")
-          lora_fan_in_fan_out = gr.Checkbox(label="LoRA Fan In Fan Out",
-                                            value=False)
-          peft = gr.Textbox(label="PEFT")
-        with gr.Row():
-          relora_steps = gr.Number(label="ReLoRA Steps")
-          relora_warmup_steps = gr.Number(label="ReLoRA Warmup Steps")
-          relora_anneal_steps = gr.Number(label="ReLoRA Anneal Steps")
-          relora_prune_ratio = gr.Number(label="ReLoRA Prune Ratio")
-          relora_cpu_offload = gr.Checkbox(label="ReLoRA CPU Offload",
-                                           value=False)
-        with gr.Row():
-          wandb_mode = gr.Textbox(label="WandB Mode")
-          wandb_project = gr.Textbox(label="WandB Project")
-          wandb_entity = gr.Textbox(label="WandB Entity")
-          wandb_watch = gr.Checkbox(label="WandB Watch", value=False)
-          wandb_name = gr.Textbox(label="WandB Name")
-          wandb_run_id = gr.Textbox(label="WandB Run ID")
-          wandb_log_model = gr.Checkbox(label="WandB Log Model", value=False)
-        with gr.Column():
-          mlflow_tracking_uri = gr.Textbox(label="MLFlow Tracking URI")
-          mlflow_experiment_name = gr.Textbox(label="MLFlow Experiment Name")
-          output_dir = gr.Textbox(label="Output Dir")
-          torch_compile = gr.Checkbox(label="Torch Compile", value=False)
-          torch_compile_backend = gr.Textbox(label="Torch Compile Backend")
-          gradient_accumulation_steps = gr.Number(
-              label="Gradient Accumulation Steps", value=1)
-          micro_batch_size = gr.Number(label="Micro Batch Size", value=2)
-          eval_batch_size = gr.Number(label="Eval Batch Size", value=2)
-          num_epochs = gr.Number(label="Number of Epochs", value=4)
-          warmup_steps = gr.Number(label="Warmup Steps", value=100)
-          warmup_ratio = gr.Number(label="Warmup Ratio")
-          learning_rate = gr.Number(label="Learning Rate",
-                                    value=0.00003,
-                                    step=1e-5)
-          lr_quadratic_warmup = gr.Checkbox(label="LR Quadratic Warmup",
-                                            value=False)
-          logging_steps = gr.Number(label="Logging Steps", value=1)
-          eval_steps = gr.Textbox(label="Eval Steps")
-          evals_per_epoch = gr.Number(label="Evals per Epoch", value=4)
-          save_strategy = gr.Textbox(label="Save Strategy")
-          save_steps = gr.Textbox(label="Save Steps")
-          saves_per_epoch = gr.Number(label="Saves per Epoch", value=1)
-          save_total_limit = gr.Number(label="Save Total Limit")
-          max_steps = gr.Number(label="Max Steps")
-          eval_table_size = gr.Number(label="Eval Table Size")
-          eval_max_new_tokens = gr.Number(label="Eval Max New Tokens",
-                                          value=128)
-          eval_causal_lm_metrics = gr.TextArea(label="Eval Causal LM Metrics")
-          loss_watchdog_threshold = gr.Number(label="Loss Watchdog Threshold")
-          loss_watchdog_patience = gr.Number(label="Loss Watchdog Patience",
-                                             value=3)
-          save_safetensors = gr.Checkbox(label="Save SafeTensors", value=False)
-          train_on_inputs = gr.Checkbox(label="Train on Inputs", value=False)
-          group_by_length = gr.Checkbox(label="Group by Length", value=False)
-          gradient_checkpointing = gr.Checkbox(label="Gradient Checkpointing",
-                                               value=False)
-          early_stopping_patience = gr.Number(label="Early Stopping Patience",
-                                              value=3)
-          lr_scheduler = gr.Textbox(label="LR Scheduler")
-          lr_scheduler_kwargs = gr.TextArea(label="LR Scheduler KWArgs")
-          cosine_min_lr_ratio = gr.Number(label="Cosine Min LR Ratio")
-          cosine_constant_lr_ratio = gr.Number(
-              label="Cosine Constant LR Ratio")
-          lr_div_factor = gr.Number(label="LR Div Factor")
-          log_sweep_min_lr = gr.Number(label="Log Sweep Min LR")
-          log_sweep_max_lr = gr.Number(label="Log Sweep Max LR")
-          optimizer = gr.Textbox(label="Optimizer")
-          weight_decay = gr.Number(label="Weight Decay", value=0.0, step=0.01)
-          adam_beta1 = gr.Number(label="Adam Beta1", value=0.9, step=0.01)
-          adam_beta2 = gr.Number(label="Adam Beta2", value=0.999, step=0.001)
-          adam_epsilon = gr.Number(label="Adam Epsilon", value=1e-8, step=1e-9)
-          max_grad_norm = gr.Number(label="Max Grad Norm")
-          neftune_noise_alpha = gr.Number(label="NEFTune Noise Alpha")
-          flash_optimum = gr.Checkbox(label="Flash Optimum", value=False)
-          xformers_attention = gr.Checkbox(label="XFormers Attention",
-                                           value=False)
-          flash_attention = gr.Checkbox(label="Flash Attention", value=False)
-          flash_attn_cross_entropy = gr.Checkbox(
-              label="Flash Attn Cross Entropy", value=False)
-          flash_attn_rms_norm = gr.Checkbox(label="Flash Attn RMS Norm",
-                                            value=False)
-          flash_attn_fuse_qkv = gr.Checkbox(label="Flash Attn Fuse QKV",
-                                            value=False)
-          flash_attn_fuse_mlp = gr.Checkbox(label="Flash Attn Fuse MLP",
-                                            value=False)
-          sdp_attention = gr.Checkbox(label="SDP Attention", value=False)
-          s2_attention = gr.Checkbox(label="S2 Attention", value=False)
-          resume_from_checkpoint = gr.Textbox(label="Resume From Checkpoint")
-          auto_resume_from_checkpoints = gr.Checkbox(
-              label="Auto Resume From Checkpoints", value=False)
-          local_rank = gr.Number(label="Local Rank")
-          special_tokens = gr.TextArea(label="Special Tokens")
-          tokens = gr.TextArea(label="Tokens")
-          fsdp = gr.Checkbox(label="FSDP", value=False)
-          fsdp_config = gr.TextArea(label="FSDP Config")
-          deepspeed = gr.Textbox(label="Deepspeed")
-          ddp_timeout = gr.Number(label="DDP Timeout")
-          ddp_bucket_cap_mb = gr.Number(label="DDP Bucket Cap MB")
-          ddp_broadcast_buffers = gr.Checkbox(label="DDP Broadcast Buffers",
-                                              value=False)
-          torchdistx_path = gr.Textbox(label="TorchDistX Path")
-          pretraining_dataset = gr.Textbox(label="Pretraining Dataset")
-          debug = gr.Checkbox(label="Debug", value=False)
-          seed = gr.Number(label="Seed", value=42)
-          strict = gr.Checkbox(label="Strict", value=False)
-
-    submit_button = gr.Button("Launch Configuration")
-    output_area = gr.TextArea(label="Configuration Output")
+import os
+
+
+class Main:
+
+    async def train_model(self,max_steps, base_model, model_type, tokenizer_type, is_llama_derived_model,
+                    strict, datasets_path, dataset_format, shards,
+                    val_set_size, output_dir, adapter, lora_model_dir, sequence_len, sample_packing,
+                    pad_to_sequence_len, lora_r, lora_alpha, lora_dropout,
+                    lora_target_modules, lora_target_linear, lora_fan_in_fan_out, gradient_accumulation_steps,
+                    micro_batch_size, num_epochs, optimizer, lr_scheduler, learning_rate, train_on_inputs,
+                    group_by_length, bf16, fp16, tf32, gradient_checkpointing,
+                    resume_from_checkpoint, local_rank, logging_steps, xformers_attention, flash_attention,
+                    load_best_model_at_end, warmup_steps, evals_per_epoch, eval_table_size, saves_per_epoch,
+                    debug, weight_decay, wandb_project, wandb_entity, wandb_watch,
+                    wandb_name, wandb_log_model,last_tab,progress=gr.Progress(track_tqdm=True)):
+
+      a = [base_model, model_type, tokenizer_type, is_llama_derived_model,
+          strict, datasets_path, dataset_format, shards,
+          val_set_size, output_dir, adapter, lora_model_dir, sequence_len, sample_packing,
+          pad_to_sequence_len, lora_r, lora_alpha, lora_dropout,
+          lora_target_modules, lora_target_linear, lora_fan_in_fan_out, gradient_accumulation_steps,
+          micro_batch_size, num_epochs, optimizer, lr_scheduler, learning_rate, train_on_inputs,
+          group_by_length, bf16, fp16, tf32, gradient_checkpointing,
+          resume_from_checkpoint, local_rank, logging_steps, xformers_attention, flash_attention,
+          load_best_model_at_end, warmup_steps, evals_per_epoch, eval_table_size, saves_per_epoch,
+          debug, weight_decay, wandb_project, wandb_entity, wandb_watch,
+          wandb_name, wandb_log_model,last_tab]
+
+      return a
+
+
+
+
+
+    def initiate_userInterface(self):
+        with gr.Blocks() as self.app:
+            gr.Markdown("### Axolotl UI")
+
+            # Finetuning Tab
+            with gr.Tab("FineTuning UI"):
+                base_model = gr.Dropdown(choices=["NousResearch/Llama-2-7b-hf", "mistralai/Mistral-7B-Instruct-v0.2"], label="Select Model", value="NousResearch/Llama-2-7b-hf")
+                datasets_path = gr.Textbox(label="datasets_path", value="mhenrichsen/alpaca_2k_test")
+                dataset_format = gr.Radio(choices=['Alpaca'], label="Dataset Format", value='Alpaca')
+                shards = gr.Slider(minimum=0, maximum=20, step=1, label="shards", value=10)
+                last_tab = gr.Checkbox(label='last_tab',value=False,visible=False)
+
+                with gr.Accordion("Advanced Settings",open=False):
+                    with gr.Tab("YAML Configuration"):
+                        model_type = gr.Radio(label="model_type", choices=['MistralForCausalLM','LlamaForCausalLM'],info="",value="LlamaForCausalLM")
+                        tokenizer_type = gr.Textbox(label="tokenizer_type", value="LlamaTokenizer",visible=False)
+                        is_llama_derived_model = gr.Checkbox(label="is_llama_derived_model", value=True,info="Determines the padding strategy based on the parent type of the model")
+                        strict = gr.Checkbox(label="strict", value=False,visible=False)
+                        val_set_size = gr.Slider(minimum=0, maximum=1, step=0.1, label="val_set_size", value=0.05,info="Percentage of training data to be used for validation")
+                        output_dir = gr.Textbox(label="output_dir", value="./finetune-out",info="Output directory of the finetuned model")
+                        adapter = gr.Radio(choices=["qlora", "lora"], label="adapter",value='qlora',info="Parameter efficient training strategy")
+                        lora_model_dir = gr.Textbox(label="lora_model_dir",info="Directory of a custom adapter can be provided",visible=False)
+                        sequence_len = gr.Slider(minimum=512, maximum=4096, step=10,label="sequence_len", value=1024,info="The maximum length input allowed to train")
+                        sample_packing = gr.Checkbox(label="sample_packing", value=True,info="Speeds up data preparation but recommended false for small datasets")
+                        pad_to_sequence_len = gr.Checkbox(label="pad_to_sequence_len", value=True, info="Pads the input to match sequence length to avoid memory fragmentation and out of memory issues. Recommended true")
+                        # eval_sample_packing = gr.Checkbox(label="eval_sample_packing", value=False)
+                        lora_r = gr.Slider(minimum=8, maximum=64, step=2,label="lora_r", value=32,info="The number of parameters in adaptation layers.")
+                        lora_alpha = gr.Slider(minimum=8, maximum=64, step=0.1,label="lora_alpha", value=16,info="How much adapted weights affect base model's")
+                        lora_dropout = gr.Slider(minimum=0, maximum=1, label="lora_dropout", value=0.05, step=0.01,info="The ratio of weights ignored randomly within adapted weights")
+                        lora_target_modules = gr.Textbox(label="lora_target_modules", value="q_proj, v_proj, k_proj",info="All dense layers can be targeted using parameter efficient tuning")
+                        lora_target_linear = gr.Checkbox(label="lora_target_linear", value=True,info="Lora Target Modules will be ignored and all linear layers will be used")
+                        lora_fan_in_fan_out = gr.Textbox(label="lora_fan_in_fan_out",visible=False)
+
+                        gradient_accumulation_steps = gr.Slider(minimum=4, maximum=64, step=1,label="gradient_accumulation_steps", value=4,info="Number of steps required to update the weights with cumulative gradients")
+                        micro_batch_size = gr.Slider(minimum=1, maximum=64, step=2,label="micro_batch_size", value=2,info="Number of samples sent to each gpu")
+                        num_epochs = gr.Slider(minimum=1, maximum=4, step=1,label="num_epochs", value=1)
+                        max_steps = gr.Textbox(label="max_steps",value='1',info="Maximum number of steps to be trained. Overwrites the number of epochs",visible=False)
+                        optimizer = gr.Radio(choices=["adamw_hf",'adamw_torch','adamw_torch_fused','adamw_torch_xla','adamw_apex_fused','adafactor','adamw_anyprecision','sgd','adagrad','adamw_bnb_8bit','lion_8bit','lion_32bit','paged_adamw_32bit','paged_adamw_8bit','paged_lion_32bit','paged_lion_8bit'], value="paged_adamw_32bit",label='optimizer',info="Use an optimizer which aligns with the quantization of model")
+                        lr_scheduler = gr.Radio(label="lr_scheduler", choices=['one_cycle', 'log_sweep', 'cosine'],value="cosine",info="Determines dynamic learning rate based on current step")
+                        learning_rate = gr.Textbox(label="max_learning_rate", value="2e-5",info="")
+                        train_on_inputs = gr.Checkbox(label="train_on_inputs", value=False,visible=False)
+                        group_by_length = gr.Checkbox(label="group_by_length", value=False,visible=False)
+                        bf16 = gr.Checkbox(label="bfloat16", value=False,info="Enable bfloat16 precision for tensors; supported only on Ampere or newer GPUs.")
+                        fp16 = gr.Checkbox(label="Half Precision", value=True,info="Enable half precision (FP16) for tensor processing.")
+                        tf32 = gr.Checkbox(label="TensorFloat32", value=False,info="Enable TensorFloat32 precision for tensors; supported only on Ampere or newer GPUs.")
+                        gradient_checkpointing = gr.Checkbox(label="gradient_checkpointing", value=True,info='',visible=False)
+                        resume_from_checkpoint = gr.Textbox(label="resume_from_checkpoint",visible=False)
+                        local_rank = gr.Textbox(label="local_rank",visible=False)
+                        logging_steps = gr.Slider(minimum=1, maximum=100, step=1,label="logging_steps", value=1,info='',visible=False)
+                        xformers_attention = gr.Checkbox(label="xformers_attention", value=False,visible=False)
+                        flash_attention = gr.Checkbox(label="flash_attention", value=False,info='',visible=False)
+                        load_best_model_at_end = gr.Checkbox(label="load_best_model_at_end", value=False,visible=False)
+                        warmup_steps = gr.Slider(minimum=1, maximum=100, step=1,label="warmup_steps", value=10,visible=False)
+                        evals_per_epoch = gr.Slider(minimum=1, maximum=100, step=1,label="evals_per_epoch", value=4,info='No. of Evaluation Per Epoch',visible=False)
+                        eval_table_size = gr.Textbox(label="eval_table_size",visible=False)
+                        saves_per_epoch = gr.Slider(minimum=1, maximum=100, step=1,label="saves_per_epoch", value=1,info='No. of checkpoints to be saved')
+
+                        debug = gr.Checkbox(label="debug", value=False,visible=False)
+
+                        weight_decay = gr.Number(label="weight_decay", value=0.0,visible=False)
+                        wandb_watch = gr.Checkbox(label="wandb_watch", value=False,visible=False)
+                        wandb_log_model = gr.Checkbox(label="wandb_log_model", value=False,visible=False)
+                        wandb_project = gr.Textbox(label="wandb_project",visible=False)
+                        wandb_entity = gr.Textbox(label="wandb_entity",visible=False)
+                        wandb_name = gr.Textbox(label="wandb_name",visible=False)
+
+
+                train_btn = gr.Button("Start Training")
+                train_btn.click(
+                    self.train_model,
+                    inputs=[max_steps, base_model, model_type, tokenizer_type, is_llama_derived_model,
+                    strict, datasets_path, dataset_format, shards,
+                    val_set_size, output_dir, adapter, lora_model_dir, sequence_len, sample_packing,
+                    pad_to_sequence_len, lora_r, lora_alpha, lora_dropout,
+                    lora_target_modules, lora_target_linear, lora_fan_in_fan_out, gradient_accumulation_steps,
+                    micro_batch_size, num_epochs, optimizer, lr_scheduler, learning_rate, train_on_inputs,
+                    group_by_length, bf16, fp16, tf32, gradient_checkpointing,
+                    resume_from_checkpoint, local_rank, logging_steps, xformers_attention, flash_attention,
+                    load_best_model_at_end, warmup_steps, evals_per_epoch, eval_table_size, saves_per_epoch,
+                    debug, weight_decay, wandb_project, wandb_entity, wandb_watch,
+                    wandb_name, wandb_log_model,last_tab],
+                    outputs=[gr.Textbox(label="Training Output",interactive=False)]
+                )
+
+        return self.app
+
+
+if __name__ == "__main__":
+    main = Main()
+    app = main.initiate_userInterface()                   
+    app.queue().launch(share=True,server_name='0.0.0.0')
 
-  submit_button.click(
-      config,
-      inputs=[
-          base_model, base_model_ignore_patterns, base_model_config,
-          model_revision, tokenizer_config, model_type, tokenizer_type,
-          trust_remote_code, tokenizer_use_fast, tokenizer_legacy,
-          resize_token_embeddings_to_32x, is_falcon_derived_model,
-          is_llama_derived_model, is_mistral_derived_model,
-          is_qwen_derived_model, model_config, bnb_config_kwargs, gptq,
-          gptq_groupsize, gptq_model_v1, load_in_8bit, load_in_4bit, bf16,
-          fp16, tf32, bfloat16, float16, gpu_memory_limit, lora_on_cpu,
-          datasets, test_datasets, rl, chat_template, default_system_message,
-          dataset_prepared_path, push_dataset_to_hub, dataset_processes,
-          dataset_keep_in_memory, hub_model_id, hub_strategy,
-          hf_use_auth_token, val_set_size, dataset_shard_num,
-          dataset_shard_idx, sequence_len, pad_to_sequence_len, sample_packing,
-          eval_sample_packing, sample_packing_eff_est, total_num_tokens,
-          device_map, max_memory, adapter, lora_model_dir, lora_r, lora_alpha,
-          lora_dropout, lora_target_modules, lora_target_linear,
-          lora_modules_to_save, lora_fan_in_fan_out, peft, relora_steps,
-          relora_warmup_steps, relora_anneal_steps, relora_prune_ratio,
-          relora_cpu_offload, wandb_mode, wandb_project, wandb_entity,
-          wandb_watch, wandb_name, wandb_run_id, wandb_log_model,
-          mlflow_tracking_uri, mlflow_experiment_name, output_dir,
-          torch_compile, torch_compile_backend, gradient_accumulation_steps,
-          micro_batch_size, eval_batch_size, num_epochs, warmup_steps,
-          warmup_ratio, learning_rate, lr_quadratic_warmup, logging_steps,
-          eval_steps, evals_per_epoch, save_strategy, save_steps,
-          saves_per_epoch, save_total_limit, max_steps, eval_table_size,
-          eval_max_new_tokens, eval_causal_lm_metrics, loss_watchdog_threshold,
-          loss_watchdog_patience, save_safetensors, train_on_inputs,
-          group_by_length, gradient_checkpointing, early_stopping_patience,
-          lr_scheduler, lr_scheduler_kwargs, cosine_min_lr_ratio,
-          cosine_constant_lr_ratio, lr_div_factor, log_sweep_min_lr,
-          log_sweep_max_lr, optimizer, weight_decay, adam_beta1, adam_beta2,
-          adam_epsilon, max_grad_norm, neftune_noise_alpha, flash_optimum,
-          xformers_attention, flash_attention, flash_attn_cross_entropy,
-          flash_attn_rms_norm, flash_attn_fuse_qkv, flash_attn_fuse_mlp,
-          sdp_attention, s2_attention, resume_from_checkpoint,
-          auto_resume_from_checkpoints, local_rank, special_tokens, tokens,
-          fsdp, fsdp_config, deepspeed, ddp_timeout, ddp_bucket_cap_mb,
-          ddp_broadcast_buffers, torchdistx_path, pretraining_dataset, debug,
-          seed, strict
-      ],
-      outputs=output_area)
-  """
-    This section is used to create a configuration file from user text.
-  """
-  with gr.Tab(label="YML text"):
-    yml_config_text = gr.TextArea(label='YML Config',
-                                  lines=50,
-                                  value=example_yml)
-    create_config = gr.Button("Create config")
-    output = gr.TextArea(label="Generated config")
-    create_config.click(
-        yml_config,
-        inputs=[yml_config_text],
-        outputs=output,
-    )
 
-demo.launch(share=True)