from config import SHARE, MODELS, TRAINING_PARAMS, LORA_TRAINING_PARAMS, GENERATION_PARAMS
import os
import gradio as gr
import random
from trainer import Trainer
LORA_DIR = 'lora'
def random_name():
fruits = [
"dragonfruit", "kiwano", "rambutan", "durian", "mangosteen",
"jabuticaba", "pitaya", "persimmon", "acai", "starfruit"
]
return '-'.join(random.sample(fruits, 3))
class UI():
def __init__(self):
self.trainer = Trainer()
def load_loras(self):
loaded_model_name = self.trainer.model_name
if os.path.exists(LORA_DIR) and loaded_model_name is not None:
loras = [f for f in os.listdir(LORA_DIR)]
sanitized_model_name = loaded_model_name.replace('/', '_').replace('.', '_')
loras = [f for f in loras if f.startswith(sanitized_model_name)]
loras.insert(0, 'None')
return gr.Dropdown.update(choices=loras)
else:
return gr.Dropdown.update(choices=['None'], value='None')
def training_params_block(self):
with gr.Row():
with gr.Column():
self.max_seq_length = gr.Slider(
interactive=True,
minimum=1, maximum=4096, value=TRAINING_PARAMS['max_seq_length'],
label="Max Sequence Length",
)
self.micro_batch_size = gr.Slider(
minimum=1, maximum=100, step=1, value=TRAINING_PARAMS['micro_batch_size'],
label="Micro Batch Size",
)
self.gradient_accumulation_steps = gr.Slider(
minimum=1, maximum=128, step=1, value=TRAINING_PARAMS['gradient_accumulation_steps'],
label="Gradient Accumulation Steps",
)
self.epochs = gr.Slider(
minimum=1, maximum=100, step=1, value=TRAINING_PARAMS['epochs'],
label="Epochs",
)
self.learning_rate = gr.Slider(
minimum=0.00001, maximum=0.01, value=TRAINING_PARAMS['learning_rate'],
label="Learning Rate",
)
with gr.Column():
self.lora_r = gr.Slider(
minimum=1, maximum=64, step=1, value=LORA_TRAINING_PARAMS['lora_r'],
label="LoRA R",
)
self.lora_alpha = gr.Slider(
minimum=1, maximum=128, step=1, value=LORA_TRAINING_PARAMS['lora_alpha'],
label="LoRA Alpha",
)
self.lora_dropout = gr.Slider(
minimum=0, maximum=1, step=0.01, value=LORA_TRAINING_PARAMS['lora_dropout'],
label="LoRA Dropout",
)
def load_model(self, model_name, progress=gr.Progress(track_tqdm=True)):
if model_name == '': return ''
if model_name is None: return self.trainer.model_name
progress(0, desc=f'Loading {model_name}...')
self.trainer.load_model(model_name)
return self.trainer.model_name
def base_model_block(self):
self.model_name = gr.Dropdown(label='Base Model', choices=MODELS)
def training_data_block(self):
training_text = gr.TextArea(
lines=20,
label="Training Data",
info='Paste training data text here. Sequences must be separated with 2 blank lines'
)
examples_dir = os.path.join(os.getcwd(), 'example-datasets')
def load_example(filename):
with open(os.path.join(examples_dir, filename) , 'r', encoding='utf-8') as f:
return f.read()
example_filename = gr.Textbox(visible=False)
example_filename.change(fn=load_example, inputs=example_filename, outputs=training_text)
gr.Examples("./example-datasets", inputs=example_filename)
self.training_text = training_text
def training_launch_block(self):
with gr.Row():
with gr.Column():
self.new_lora_name = gr.Textbox(label='New PEFT Adapter Name', value=random_name())
with gr.Column():
train_button = gr.Button('Train', variant='primary')
def train(
training_text,
new_lora_name,
max_seq_length,
micro_batch_size,
gradient_accumulation_steps,
epochs,
learning_rate,
lora_r,
lora_alpha,
lora_dropout,
progress=gr.Progress(track_tqdm=True)
):
self.trainer.unload_lora()
self.trainer.train(
training_text,
new_lora_name,
max_seq_length=max_seq_length,
micro_batch_size=micro_batch_size,
gradient_accumulation_steps=gradient_accumulation_steps,
epochs=epochs,
learning_rate=learning_rate,
lora_r=lora_r,
lora_alpha=lora_alpha,
lora_dropout=lora_dropout
)
return new_lora_name
train_button.click(
fn=train,
inputs=[
self.training_text,
self.new_lora_name,
self.max_seq_length,
self.micro_batch_size,
self.gradient_accumulation_steps,
self.epochs,
self.learning_rate,
self.lora_r,
self.lora_alpha,
self.lora_dropout,
],
outputs=[self.new_lora_name]
).then(
fn=lambda x: self.trainer.load_model(x, force=True),
inputs=[self.model_name],
outputs=[]
)
def inference_block(self):
with gr.Row():
with gr.Column():
self.lora_name = gr.Dropdown(
interactive=True,
choices=['None'],
value='None',
label='LoRA',
)
def load_lora(lora_name, progress=gr.Progress(track_tqdm=True)):
if lora_name == 'None':
self.trainer.unload_lora()
else:
self.trainer.load_lora(f'{LORA_DIR}/{lora_name}')
return lora_name
self.lora_name.change(
fn=load_lora,
inputs=self.lora_name,
outputs=self.lora_name
)
self.prompt = gr.Textbox(
interactive=True,
lines=5,
label="Prompt",
value="Human: How is cheese made?\nAssistant:"
)
self.generate_btn = gr.Button('Generate', variant='primary')
with gr.Row():
with gr.Column():
self.max_new_tokens = gr.Slider(
minimum=0, maximum=4096, step=1, value=GENERATION_PARAMS['max_new_tokens'],
label="Max New Tokens",
)
with gr.Column():
self.do_sample = gr.Checkbox(
interactive=True,
label="Enable Sampling (leave off for greedy search)",
value=True,
)
with gr.Row():
with gr.Column():
self.num_beams = gr.Slider(
minimum=1, maximum=10, step=1, value=GENERATION_PARAMS['num_beams'],
label="Num Beams",
)
with gr.Column():
self.repeat_penalty = gr.Slider(
minimum=0, maximum=4.5, step=0.01, value=GENERATION_PARAMS['repetition_penalty'],
label="Repetition Penalty",
)
with gr.Row():
with gr.Column():
self.temperature = gr.Slider(
minimum=0.01, maximum=1.99, step=0.01, value=GENERATION_PARAMS['temperature'],
label="Temperature",
)
self.top_p = gr.Slider(
minimum=0, maximum=1, step=0.01, value=GENERATION_PARAMS['top_p'],
label="Top P",
)
self.top_k = gr.Slider(
minimum=0, maximum=200, step=1, value=GENERATION_PARAMS['top_k'],
label="Top K",
)
with gr.Column():
self.output = gr.Textbox(
interactive=True,
lines=20,
label="Output"
)
def generate(
prompt,
do_sample,
max_new_tokens,
num_beams,
repeat_penalty,
temperature,
top_p,
top_k,
progress=gr.Progress(track_tqdm=True)
):
return self.trainer.generate(
prompt,
do_sample=do_sample,
max_new_tokens=max_new_tokens,
num_beams=num_beams,
repetition_penalty=repeat_penalty,
temperature=temperature,
top_p=top_p,
top_k=top_k
)
self.generate_btn.click(
fn=generate,
inputs=[
self.prompt,
self.do_sample,
self.max_new_tokens,
self.num_beams,
self.repeat_penalty,
self.temperature,
self.top_p,
self.top_k
],
outputs=[self.output]
)
def layout(self):
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
gr.HTML("""<h2>
<a style="text-decoration: none;" href="https://github.com/lxe/simple-llama-finetuner">🦙 Simple LLM Finetuner</a> <a href="https://huggingface.co/spaces/lxe/simple-llama-finetuner?duplicate=true"><img
src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" style="display:inline">
</a></h2><p>Finetune an LLM on your own text. Duplicate this space onto a GPU-enabled space to run.</p>""")
with gr.Column():
self.base_model_block()
with gr.Tab('Finetuning'):
with gr.Row():
with gr.Column():
self.training_data_block()
with gr.Column():
self.training_params_block()
self.training_launch_block()
with gr.Tab('Inference') as inference_tab:
with gr.Row():
with gr.Column():
self.inference_block()
inference_tab.select(
fn=self.load_loras,
inputs=[],
outputs=[self.lora_name]
)
self.model_name.change(
fn=self.load_model,
inputs=[self.model_name],
outputs=[self.model_name]
).then(
fn=self.load_loras,
inputs=[],
outputs=[self.lora_name]
)
return demo
def run(self):
self.ui = self.layout()
self.ui.queue().launch(show_error=True, share=SHARE)
if (__name__ == '__main__'):
ui = UI()
ui.run()