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import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
import torch
from threading import Thread
# Load model and tokenizer
model_name = "GoofyLM/BrainrotLM-Assistant"
model = AutoModelForCausalLM.from_pretrained(
model_name,
device_map="auto",
torch_dtype=torch.float16
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Set pad token if missing
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
# Define a custom chat template if one is not available
if tokenizer.chat_template is None:
# Basic ChatML-style template
tokenizer.chat_template = "{% for message in messages %}\n{% if message['role'] == 'system' %}<|system|>\n{{ message['content'] }}\n{% elif message['role'] == 'user' %}<|user|>\n{{ message['content'] }}\n{% elif message['role'] == 'assistant' %}<|assistant|>\n{{ message['content'] }}\n{% endif %}\n{% endfor %}\n{% if add_generation_prompt %}<|assistant|>\n{% endif %}"
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
# Build conversation messages
messages = [{"role": "system", "content": system_message}]
for user_msg, assistant_msg in history:
if user_msg:
messages.append({"role": "user", "content": user_msg})
if assistant_msg:
messages.append({"role": "assistant", "content": assistant_msg})
messages.append({"role": "user", "content": message})
# Format prompt using chat template
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
# Set up streaming
streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
# Configure generation parameters
do_sample = temperature > 0 or top_p < 1.0
generation_kwargs = dict(
**inputs,
streamer=streamer,
max_new_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
do_sample=do_sample,
pad_token_id=tokenizer.pad_token_id
)
# Start generation in separate thread
thread = Thread(target=model.generate, kwargs=generation_kwargs)
thread.start()
# Stream response
response = ""
for token in streamer:
response += token
yield response
# Create Gradio interface
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Slider(1, 2048, value=72, label="Max new tokens"),
gr.Slider(0.1, 4.0, value=0.7, label="Temperature"),
gr.Slider(0.1, 1.0, value=0.95, label="Top-p (nucleus sampling)"),
],
)
if __name__ == "__main__":
demo.launch() |