app.py

import gradio as gr
import transformers
import torch
import json
from transformers import AutoTokenizer
import os
from huggingface_hub import login
import spaces

HF_TOKEN = os.getenv("HF_TOKEN")
login(HF_TOKEN)
# Load the model
model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id, add_special_tokens=True)

pipeline = transformers.pipeline(
    "text-generation",
    model=model_id,
    model_kwargs={"torch_dtype": torch.bfloat16},
    device="cuda",
)

# Load the model configuration
with open("model_configs.json", "r") as f:
    model_configs = json.load(f)
    model_config = model_configs[model_id]

# Extract instruction
extract_input = model_config["extract_input"]


@spaces.GPU
def generate_instruction_response():
    terminators = [
        tokenizer.eos_token_id,
        tokenizer.convert_tokens_to_ids("<|eot_id|>"),
    ]

    instruction = pipeline(
        extract_input,
        max_new_tokens=2048,
        eos_token_id=terminators,
        do_sample=True,
        temperature=1,
        top_p=1,
    )

    sanitized_instruction = instruction[0]["generated_text"][
        len(extract_input) :
    ].split("\n")[0]

    response_template = f"""<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\n{sanitized_instruction}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"""

    response = pipeline(
        response_template,
        max_new_tokens=2048,
        eos_token_id=terminators,
        do_sample=True,
        temperature=1,
        top_p=1,
    )

    user_message = sanitized_instruction
    assistant_response = response[0]["generated_text"][len(response_template) :]

    return user_message, assistant_response


title = "Magpie demo"
description = """
This Gradio demo allows you to explore the approach outlined in the Magpie paper. "Magpie is a data synthesis pipeline that generates high-quality alignment data. Magpie does not rely on prompt engineering or seed questions. Instead, it directly constructs instruction data by prompting aligned LLMs with a pre-query template for sampling instructions." Essentially, instead of prompting the model with a question or a starting query, this approach relies on the pre-query template of the model to generate instructions. Essentially, you are giving the model only the template up to the point where a user instruction would start, and then the model generates the instruction and the response. 

In this demo, you can see how the model generates a user instruction and a model response.

You can learn more about the approach [in the paper](https://huggingface.co/papers/2406.08464).
"""
# Create the Gradio interface
iface = gr.Interface(
    fn=generate_instruction_response,
    inputs=[],
    outputs=[
        gr.Text(label="Generated User Instruction"),
        gr.Text(label="Generated Model Response"),
    ],
    title=title,
    description=description,
)

# Launch the app
iface.launch(debug=True)