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import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# Load the model and tokenizer locally
model_name = "mergekit-community/Anti-Qwen2.5-Coder-0.5B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16)
# Ensure the model runs on GPU if available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
# Prepare the conversation history for the model
messages = [f"System: {system_message}"]
for val in history:
if val[0]:
messages.append(f"User: {val[0]}")
if val[1]:
messages.append(f"Assistant: {val[1]}")
messages.append(f"User: {message}")
context = "\n".join(messages)
# Tokenize input
input_ids = tokenizer(context, return_tensors="pt", truncation=True, max_length=2048).input_ids.to(device)
# Generate response
output_ids = model.generate(
input_ids,
max_new_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
do_sample=True,
pad_token_id=tokenizer.eos_token_id,
)
# Decode and yield response
response = tokenizer.decode(output_ids[0][input_ids.shape[-1]:], skip_special_tokens=True)
yield response
# Gradio interface setup
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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