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##################################### Imports ######################################
# Generic imports
import gradio as gr
import json
# Module imports
from utilities.setup import get_json_cfg
from utilities.templates import prompt_template
########################### Global objects and functions ###########################
conf = get_json_cfg()
def textbox_visibility(radio):
value = radio
if value == "Hugging Face Hub Dataset":
return gr.Dropdown(visible=bool(1))
else:
return gr.Dropdown(visible=bool(0))
def upload_visibility(radio):
value = radio
if value == "Upload Your Own":
return gr.UploadButton(visible=bool(1)) #make it visible
else:
return gr.UploadButton(visible=bool(0))
def greet(model_name, inject_prompt, dataset, pefts):
"""The model call"""
return f"Hello!! Using model: {model_name} with template: {inject_prompt}"
##################################### App UI #######################################
def main():
with gr.Blocks() as demo:
##### Title Block #####
gr.Markdown("# Instruction Tuning with Unsloth")
##### 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 Dataset",
value='yahma/alpaca-cleaned',
visible=True)
dataset_upload = gr.UploadButton(label="Upload Dataset (csv, jsonl, or txt)",
file_types=[".csv",".jsonl", ".txt"],
visible=False)
data_field = gr.Textbox(label="Dataset Training Field",
value=conf['model']['general']["dataset_text_field"])
dataset_choice.change(textbox_visibility,
dataset_choice,
dataset_predefined)
dataset_choice.change(upload_visibility,
dataset_choice,
dataset_upload)
gr.Markdown("### Model Parameter Selection")
# Parameters
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 button
tune_btn = gr.Button("Start Fine Tuning")
gr.Markdown("### Model Progress")
# Text output (for now)
output = gr.Textbox(label="Output")
# Execute button
tune_btn.click(fn=greet,
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)
# Launch baby
demo.launch()
##################################### Launch #######################################
if __name__ == "__main__":
main() |