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import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
import spaces
from threading import Thread
from typing import Iterator
# Add markdown header
header = """
# 🐦⬛ MagpieLMs: Open LLMs with Fully Transparent Alignment Recipes
💬 We've aligned Llama-3.1-8B and a 4B version (distilled by NVIDIA) using purely synthetic data generated by our [Magpie](https://arxiv.org/abs/2406.08464) method. Our open-source post-training recipe includes: SFT and DPO data, all training configs + logs. This allows everyone to reproduce the alignment process for their own research. Note that our data does not contain any GPT-generated data, and has a much friendly license for both commercial and academic use.
🔗 Links: [**Magpie Collection**](https://huggingface.co/collections/Magpie-Align/magpielm-66e2221f31fa3bf05b10786a); [**Magpie Paper**](https://arxiv.org/abs/2406.08464) 📮 Contact: [Zhangchen Xu](https://zhangchenxu.com) and [Bill Yuchen Lin](https://yuchenlin.xyz).
---
"""
# Load model and tokenizer
model_name = "Magpie-Align/MagpieLM-8B-Chat-v0.1"
device = "cuda" # the device to load the model onto
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
ignore_mismatched_sizes=True
)
model.to(device)
MAX_INPUT_TOKEN_LENGTH = 4096 # You may need to adjust this value
@spaces.GPU
def respond(
message: str,
chat_history: list[tuple[str, str]],
system_prompt: str,
max_new_tokens: int = 1024,
temperature: float = 0.6,
top_p: float = 0.9,
top_k: int = 50,
repetition_penalty: float = 1.2,
) -> Iterator[str]:
conversation = []
if system_prompt:
conversation.append({"role": "system", "content": system_prompt})
for user, assistant in chat_history:
conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}])
conversation.append({"role": "user", "content": message})
input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt")
if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH:
input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:]
gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.")
input_ids = input_ids.to(model.device)
streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)
generate_kwargs = dict(
input_ids=input_ids,
streamer=streamer,
max_new_tokens=max_new_tokens,
do_sample=True,
top_p=top_p,
top_k=top_k,
temperature=temperature,
num_beams=1,
repetition_penalty=repetition_penalty,
)
t = Thread(target=model.generate, kwargs=generate_kwargs)
t.start()
outputs = []
for text in streamer:
outputs.append(text)
yield "".join(outputs)
chatbot = gr.Chatbot(placeholder="<strong>MagpieLM-Chat-8B (v0.1)</strong>")
demo = gr.ChatInterface(
fn=respond,
chatbot=chatbot,
additional_inputs=[
gr.Textbox(value="You are Magpie, a helpful AI assistant. For simple queries, try to answer them directly; for complex questions, try to think step-by-step before providing an answer.", label="System message"),
gr.Slider(minimum=128, maximum=2048, value=512, step=64, 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=0.9,
step=0.1,
label="Top-p (nucleus sampling)",
),
gr.Slider(minimum=0.5, maximum=1.5, value=1.0, step=0.1, label="Repetition Penalty"),
],
description=header, # Add the header as the description
title="MagpieLM-8B Chat (v0.1)",
theme=gr.themes.Soft(),
examples=[
["Hello, what is your name?"],
["Can you write a poem for me?"],
["What's the meaning of life?"],
]
)
# set a default message in the chatbox to start the conversation
# demo.chatbot.placeholder = "Hello! What's your name?"
if __name__ == "__main__":
demo.queue()
demo.launch(share=True, show_api=False)
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