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import gradio as gr
from huggingface_hub import InferenceClient
from transformers import AutoModelForCausalLM, Trainer, TrainingArguments
from datasets import load_dataset
"""
For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
"""
client = InferenceClient("meta-llama/Meta-Llama-3-8B-Instruct")
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
messages = [{"role": "system", "content": system_message}]
for val in history:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
messages.append({"role": "assistant", "content": val[1]})
messages.append({"role": "user", "content": message})
response = ""
for message in client.chat_completion(
messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
token = message.choices[0].delta.content
response += token
yield response
def train_model():
os.environ["HUGGINGFACE_TOKEN"] = hf_token
# Load dataset
dataset = load_dataset('json', data_files={
'train': 'training_set.json'})
# Load model
model = AutoModelForCausalLM.from_pretrained('meta-llama/Meta-Llama-3-8B-Instruct')
# Define training arguments
training_args = TrainingArguments(
output_dir='./results',
num_train_epochs=3,
per_device_train_batch_size=16,
save_steps=10_000,
save_total_limit=2,
)
# Initialize Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=dataset['train'],
eval_dataset=dataset['test']
)
# Start training
trainer.train()
return "Training complete"
"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.Blocks()
with demo:
gr.Markdown("# Llama3training Chatbot and Model Trainer")
with gr.Tab("Chat"):
gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value="You are a friendly Chatbot.",
label="System message"),
gr.Slider(minimum=1, maximum=2048, value=512,
step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.7,
step=0.1, label="Temperature"),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.95,
step=0.05,
label="Top-p (nucleus sampling)",
),
],
)
with gr.Tab("Train"):
hf_token = gr.Textbox(label="Hugging Face Token", type="password")
train_button = gr.Button("Start Training")
train_output = gr.Textbox(label="Training Output")
train_button.click(train_model, outputs=train_output)
if __name__ == "__main__":
demo.launch()
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