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import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load the model locally
model_name = "bigchestnut/mob213"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype=torch.float16)
def respond(message, history, system_message, max_tokens, temperature, top_p):
# Prepare input prompt
prompt = system_message + "\n" + "\n".join(
[f"User: {h[0]}\nAssistant: {h[1]}" for h in history if h[0] and h[1]]
) + f"\nUser: {message}\nAssistant:"
inputs = tokenizer(prompt, return_tensors="pt")
with torch.no_grad():
outputs = model.generate(**inputs, max_new_tokens=max_tokens, temperature=temperature, top_p=top_p)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
return response
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value="You are a helpful assistant.", 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(share=True)
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