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import os
from threading import Thread
from typing import Iterator
import gradio as gr
import spaces
import torch
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
TextIteratorStreamer,
LlamaTokenizer,
)
MAX_MAX_NEW_TOKENS = 1024
DEFAULT_MAX_NEW_TOKENS = 50
MAX_INPUT_TOKEN_LENGTH = 512
DESCRIPTION = """\
# Phi-3-mini-4k-instruct
This Space demonstrates [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) by Microsoft. Please, check the original model card for details.
For additional detail on the model, including a link to the arXiv paper, refer to the [Hugging Face Paper page for Phi 3](https://huggingface.co/papers/2404.14219) .
"""
model = AutoModelForCausalLM.from_pretrained(
"microsoft/Phi-3-mini-4k-instruct",
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-4k-instruct")
if tokenizer.pad_token == None:
tokenizer.pad_token = tokenizer.eos_token
tokenizer.pad_token_id = tokenizer.eos_token_id
model.config.pad_token_id = tokenizer.eos_token_id
def generate(
message: str,
chat_history: list[tuple[str, str]],
max_new_tokens: int = 1024,
temperature: float = 0.1,
top_p: float = 0.4,
top_k: int = 10,
repetition_penalty: float = 1.4,
) -> Iterator[str]:
historical_text = ""
#Prepend the entire chat history to the message with new lines between each message
for user, assistant in chat_history:
historical_text += f"\n{user}\n{assistant}"
if len(historical_text) > 0:
message = historical_text + f"\n{message}"
input_ids = tokenizer([message], return_tensors="pt").input_ids
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=30.0, 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,
pad_token_id = tokenizer.eos_token_id,
repetition_penalty=repetition_penalty,
no_repeat_ngram_size=5,
early_stopping=False,
)
t = Thread(target=model.generate, kwargs=generate_kwargs)
t.start()
outputs = []
for text in streamer:
outputs.append(text)
yield "".join(outputs)
chat_interface = gr.ChatInterface(
fn=generate,
additional_inputs=[
gr.Slider(
label="Max new tokens",
minimum=1,
maximum=MAX_MAX_NEW_TOKENS,
step=1,
value=DEFAULT_MAX_NEW_TOKENS,
),
gr.Slider(
label="Temperature",
minimum=0.1,
maximum=4.0,
step=0.1,
value=0.1,
),
gr.Slider(
label="Top-p (nucleus sampling)",
minimum=0.05,
maximum=1.0,
step=0.05,
value=0.5,
),
gr.Slider(
label="Top-k",
minimum=1,
maximum=1000,
step=1,
value=3,
),
gr.Slider(
label="Repetition penalty",
minimum=1.0,
maximum=2.0,
step=0.05,
value=1.4,
),
],
stop_btn=None,
cache_examples=False,
examples=[
["Explain quantum physics in 5 words or less:"],
["Question: What do you call a bear with no teeth?\nAnswer:"],
],
)
with gr.Blocks(css="style.css") as demo:
gr.Markdown(DESCRIPTION)
chat_interface.render()
if __name__ == "__main__":
demo.queue(max_size=20).launch()
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