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import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
model_name = "anasmkh/customized_llama3.1_8b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype=torch.float16)
generator = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=64,
temperature=1.5,
min_p=0.1,
)
def chat(message, history):
history = history or []
history.append({"role": "user", "content": message})
response = generator(history)[-1]["generated_text"]
history.append({"role": "assistant", "content": response})
return history
with gr.Blocks() as demo:
chatbot = gr.Chatbot()
message = gr.Textbox()
clear = gr.ClearButton([message, chatbot])
message.submit(chat, [message, chatbot], chatbot)
clear.click(lambda: None, None, chatbot, queue=False)
demo.launch()
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