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import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
from peft import PeftModel
from threading import Thread
import torch
# Load base model + your adapter
print("Loading base model...")
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")
model = AutoModelForCausalLM.from_pretrained(
"meta-llama/Llama-3.1-8B-Instruct",
device_map="auto",
torch_dtype=torch.float16,
load_in_8bit=True,
)
print("Loading your fine-tuned adapter...")
model = PeftModel.from_pretrained(model, "drumwell/autotrain-2duhi-5mmyz")
print("Model loaded!")
def respond(
message,
history: list[dict[str, str]],
system_message,
max_tokens,
temperature,
top_p,
hf_token: gr.OAuthToken,
):
messages = [{"role": "system", "content": system_message}]
messages.extend(history)
messages.append({"role": "user", "content": message})
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
generation_kwargs = dict(
inputs,
streamer=streamer,
max_new_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
do_sample=True,
repetition_penalty=1.1,
)
thread = Thread(target=model.generate, kwargs=generation_kwargs)
thread.start()
response = ""
for token in streamer:
response += token
yield response
chatbot = gr.ChatInterface(
respond,
type="messages",
additional_inputs=[
gr.Textbox(value="You are a BMW E30 M3 and 320is technical expert assistant. Answer accurately based on factory specifications.", 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=0.3, 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.Blocks() as demo:
with gr.Sidebar():
gr.LoginButton()
chatbot.render()
if __name__ == "__main__":
demo.launch() |