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| ##################################### Imports ###################################### | |
| # Generic imports | |
| import gradio as gr | |
| import json | |
| # Specialized imports | |
| #from utilities.modeling import modeling | |
| from datasets import load_dataset | |
| # Module imports | |
| from utilities.setup import get_json_cfg | |
| from utilities.templates import prompt_template | |
| ########################### Global objects and functions ########################### | |
| conf = get_json_cfg() | |
| class update_visibility: | |
| def textbox_vis(radio): | |
| value = radio | |
| if value == "Hugging Face Hub Dataset": | |
| return gr.Dropdown(visible=bool(1)) | |
| else: | |
| return gr.Dropdown(visible=bool(0)) | |
| def textbox_button_vis(radio): | |
| value = radio | |
| if value == "Hugging Face Hub Dataset": | |
| return gr.Button(visible=bool(1)) | |
| else: | |
| return gr.Button(visible=bool(0)) | |
| def upload_vis(radio): | |
| value = radio | |
| if value == "Upload Your Own": | |
| return gr.UploadButton(visible=bool(1)) #make it visible | |
| else: | |
| return gr.UploadButton(visible=bool(0)) | |
| class get_datasets: | |
| def predefined_dataset(dataset_name): | |
| global dataset # bad practice, I know... But just bear with me. Will later update to state dict. | |
| dataset = load_dataset(dataset_name, split = "train") | |
| return 'Successfully loaded dataset' | |
| def uploaded_dataset(file): | |
| global dataset # bad practice, I know... But just bear with me. Will later update to state dict. | |
| dataset = [] | |
| if file is None: | |
| return "File not found. Please upload the file again." | |
| try: | |
| with open(file,'r') as file: | |
| for line in file: | |
| dataset.append(json.loads(line.strip())) | |
| return "File retrieved." | |
| except FileNotFoundError: | |
| return "File not found. Please upload the file again." | |
| def about_page(): | |
| return "## About\n\nThis is an application for uploading datasets. You can upload files in .csv, .jsonl, or .txt format. The app will process the file and provide feedback." | |
| def show_about(): | |
| return "## About\n\nThis is an application for uploading datasets. You can upload files in .csv, .jsonl, or .txt format. The app will process the file and provide feedback." | |
| def show_upload(): | |
| return "## Upload\n\nUse the button below to upload your dataset." | |
| def train(model_name, | |
| inject_prompt, | |
| dataset_predefined, | |
| peft, | |
| sft, | |
| max_seq_length, | |
| random_seed, | |
| num_epochs, | |
| max_steps, | |
| data_field, | |
| repository, | |
| model_out_name): | |
| """The model call""" | |
| # Get models | |
| # trainer = modeling(model_name, max_seq_length, random_seed, | |
| # peft, sft, dataset, data_field) | |
| # trainer_stats = trainer.train() | |
| # Return outputs of training. | |
| return f"Hello!! Using model: {model_name} with template: {inject_prompt}" | |
| def submit_weights(model, repository, model_out_name, token): | |
| """submits model to repository""" | |
| repo = repository + '/' + model_out_name | |
| model.push_to_hub(repo, token = token) | |
| tokenizer.push_to_hub(repo, token = token) | |
| return 0 | |
| ##################################### App UI ####################################### | |
| def main(): | |
| with gr.Blocks() as demo: | |
| with gr.Tabs(): | |
| with gr.TabItem("About"): | |
| gr.Markdown("## About\n\nThis is an application for uploading datasets. You can upload files in .csv, .jsonl, or .txt format. The app will process the file and provide feedback.") | |
| with gr.TabItem("Train Model"): | |
| ##### Title Block ##### | |
| gr.Markdown("# SLM Instruction Tuning with Unsloth") | |
| ##### Initial Model Inputs ##### | |
| gr.Markdown("### Model Inputs") | |
| # Select Model | |
| modelnames = conf['model']['choices'] | |
| model_name = gr.Dropdown(label="Supported Models", | |
| choices=modelnames, | |
| value=modelnames[0]) | |
| # Prompt template | |
| inject_prompt = gr.Textbox(label="Prompt Template", | |
| value=prompt_template()) | |
| # Dataset choice | |
| dataset_choice = gr.Radio(label="Choose Dataset", | |
| choices=["Hugging Face Hub Dataset", "Upload Your Own"], | |
| value="Hugging Face Hub Dataset") | |
| dataset_predefined = gr.Textbox(label="Hugging Face Hub Training Dataset", | |
| value='yahma/alpaca-cleaned', | |
| visible=True) | |
| dataset_predefined_load = gr.Button("Upload Dataset (.csv, .jsonl, or .txt)") | |
| dataset_uploaded_load = gr.UploadButton(label="Upload Dataset (.csv, .jsonl, or .txt)", | |
| file_types=[".csv",".jsonl", ".txt"], | |
| visible=False) | |
| data_snippet = gr.Markdown() | |
| dataset_choice.change(update_visibility.textbox_vis, | |
| dataset_choice, | |
| dataset_predefined) | |
| dataset_choice.change(update_visibility.upload_vis, | |
| dataset_choice, | |
| dataset_uploaded_load) | |
| dataset_choice.change(update_visibility.textbox_button_vis, | |
| dataset_choice, | |
| dataset_predefined_load) | |
| # Dataset button | |
| dataset_predefined_load.click(fn=get_datasets.predefined_dataset, | |
| inputs=dataset_predefined, | |
| outputs=data_snippet) | |
| dataset_uploaded_load.click(fn=get_datasets.uploaded_dataset, | |
| inputs=dataset_uploaded_load, | |
| outputs=data_snippet) | |
| ##### Model Parameter Inputs ##### | |
| gr.Markdown("### Model Parameter Selection") | |
| # Parameters | |
| data_field = gr.Textbox(label="Dataset Training Field Name", | |
| value=conf['model']['general']["dataset_text_field"]) | |
| max_seq_length = gr.Textbox(label="Maximum sequence length", | |
| value=conf['model']['general']["max_seq_length"]) | |
| random_seed = gr.Textbox(label="Seed", | |
| value=conf['model']['general']["seed"]) | |
| num_epochs = gr.Textbox(label="Training Epochs", | |
| value=conf['model']['general']["num_train_epochs"]) | |
| max_steps = gr.Textbox(label="Maximum steps", | |
| value=conf['model']['general']["max_steps"]) | |
| repository = gr.Textbox(label="Repository Name", | |
| value=conf['model']['general']["repository"]) | |
| model_out_name = gr.Textbox(label="Model Output Name", | |
| value=conf['model']['general']["model_name"]) | |
| # Hyperparameters (allow selection, but hide in accordion.) | |
| with gr.Accordion("Advanced Tuning", open=False): | |
| sftparams = conf['model']['general'] | |
| # accordion container content | |
| dict_string = json.dumps(dict(conf['model']['peft']), indent=4) | |
| peft = gr.Textbox(label="PEFT Parameters (json)", value=dict_string) | |
| dict_string = json.dumps(dict(conf['model']['sft']), indent=4) | |
| sft = gr.Textbox(label="SFT Parameters (json)", value=dict_string) | |
| ##### Execution ##### | |
| # Setup buttons | |
| tune_btn = gr.Button("Start Fine Tuning") | |
| gr.Markdown("### Model Progress") | |
| # Text output (for now) | |
| output = gr.Textbox(label="Output") | |
| # Data retrieval | |
| # Execute buttons | |
| tune_btn.click(fn=train, | |
| inputs=[model_name, | |
| inject_prompt, | |
| dataset_predefined, | |
| peft, | |
| sft, | |
| max_seq_length, | |
| random_seed, | |
| num_epochs, | |
| max_steps, | |
| data_field, | |
| repository, | |
| model_out_name | |
| ], | |
| outputs=output) | |
| # stop button | |
| # submit button | |
| # Launch baby | |
| demo.launch() | |
| ##################################### Launch ####################################### | |
| if __name__ == "__main__": | |
| main() |