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import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
# Load the model and tokenizer (you may need to adjust device_map or other settings depending on your hardware)
tokenizer = AutoTokenizer.from_pretrained("TheBloke/Chronoboros-33B-GPTQ")
model = AutoModelForCausalLM.from_pretrained("TheBloke/Chronoboros-33B-GPTQ", device_map="auto")
def respond(message, history: list[tuple[str, str]], system_message, max_tokens, temperature, top_p):
# Build the prompt using conversation history
prompt = f"{system_message}\n"
for user_text, assistant_text in history:
if user_text:
prompt += f"User: {user_text}\n"
if assistant_text:
prompt += f"Assistant: {assistant_text}\n"
prompt += f"User: {message}\nAssistant: "
# Tokenize the prompt and generate a response
input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.device)
output_ids = model.generate(
input_ids,
max_new_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
do_sample=True, # enable sampling for varied responses
)
# Get only the newly generated tokens (after the prompt)
new_tokens = output_ids[0][input_ids.shape[1]:]
# Simulate streaming by yielding partial responses token by token
for i in range(new_tokens.shape[0]):
current_response = tokenizer.decode(new_tokens[: i + 1], skip_special_tokens=True)
yield current_response
# Configure the ChatInterface with additional inputs
demo = 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)",
),
],
)
if __name__ == "__main__":
demo.launch()
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