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from transformers import AutoTokenizer
import transformers
import os
import sys
import fire
import torch
import gradio as gr
def main(
base_model="ise-uiuc/Magicoder-S-DS-6.7B",
#device="cuda:0",
port=8080,
):
tokenizer = AutoTokenizer.from_pretrained(base_model)
pipeline = transformers.pipeline(
"text-generation",
model=base_model,
torch_dtype=torch.float16,
device=device
)
def evaluate_magicoder(
instruction,
temperature=1,
max_new_tokens=2048,
):
MAGICODER_PROMPT = """You are an exceptionally intelligent coding assistant that consistently delivers accurate and reliable responses to user instructions.
@@ Instruction
{instruction}
@@ Response
"""
prompt = MAGICODER_PROMPT.format(instruction=instruction)
sequences = pipeline(
prompt,
temperature=temperature,
max_new_tokens=max_new_tokens,
)
for seq in sequences:
print('==========================question=============================')
print(prompt)
generated_text = seq['generated_text'].replace(prompt, "")
print('===========================answer=============================')
print(generated_text)
return generated_text
gr.Interface(
fn=evaluate_magicoder,
inputs=[
gr.components.Textbox(
lines=3, label="Instruction", placeholder="Anything you want to ask Magicoder ?"
),
gr.components.Slider(minimum=0, maximum=1, value=0, label="Temperature"),
gr.components.Slider(
minimum=1, maximum=2048, step=1, value=128, label="Max tokens"
),
],
outputs=[
gr.components.Textbox(
lines=30,
label="Output",
)
],
title="Magicoder",
description="This is a LLM playground for Magicoder! Follow us on Github: https://github.com/ise-uiuc/magicoder and Huggingface: https://huggingface.co/ise-uiuc."
).queue().launch(share=True, server_port=port)
if __name__ == "__main__":
fire.Fire(main)
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