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import gradio as gr | ||
import argparse | ||
import os | ||
import json | ||
from vllm import LLM, SamplingParams | ||
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def parse_args(): | ||
parser = argparse.ArgumentParser() | ||
parser.add_argument("--base_model", type=str) # model path | ||
parser.add_argument("--n_gpus", type=int, default=1) # n_gpu | ||
return parser.parse_args() | ||
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def predict(message, history, system_prompt, temperature, max_tokens): | ||
instruction = "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. " | ||
for human, assistant in history: | ||
instruction += 'USER: '+ human + ' ASSISTANT: '+ assistant + '</s>' | ||
instruction += 'USER: '+ message + ' ASSISTANT:' | ||
problem = [instruction] | ||
stop_tokens = ["USER:", "USER", "ASSISTANT:", "ASSISTANT"] | ||
sampling_params = SamplingParams(temperature=temperature, top_p=1, max_tokens=max_tokens, stop=stop_tokens) | ||
completions = llm.generate(problem, sampling_params) | ||
for output in completions: | ||
prompt = output.prompt | ||
print('==========================question=============================') | ||
print(prompt) | ||
generated_text = output.outputs[0].text | ||
print('===========================answer=============================') | ||
print(generated_text) | ||
for idx in range(len(generated_text)): | ||
yield generated_text[:idx+1] | ||
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if __name__ == "__main__": | ||
args = parse_args() | ||
llm = LLM(model=args.base_model, tensor_parallel_size=args.n_gpus) | ||
gr.ChatInterface( | ||
predict, | ||
title="LLM playground - WizardLM-13B-V1.2", | ||
description="This is a LLM playground for WizardLM-13B-V1.2, github: https://github.com/nlpxucan/WizardLM, huggingface: https://huggingface.co/WizardLM", | ||
theme="soft", | ||
chatbot=gr.Chatbot(height=1400, label="Chat History",), | ||
textbox=gr.Textbox(placeholder="input", container=False, scale=7), | ||
retry_btn=None, | ||
undo_btn="Delete Previous", | ||
clear_btn="Clear", | ||
additional_inputs=[ | ||
gr.Textbox("A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.", label="System Prompt"), | ||
gr.Slider(0, 1, 0.9, label="Temperature"), | ||
gr.Slider(100, 2048, 1024, label="Max Tokens"), | ||
], | ||
additional_inputs_accordion_name="Parameters", | ||
).queue().launch(share=False, server_port=7870) |