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import sys
from threading import Thread
import gradio as gr
import spaces
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
TextIteratorStreamer,
BitsAndBytesConfig,
)
import torch
MODEL = "microsoft/Phi-3.5-mini-instruct"
if torch.cuda.is_available():
device = "cuda"
elif sys.platform == "darwin" and torch.backends.mps.is_available():
device = "mps"
else:
device = "cpu"
# TODO understand this
quantization_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
)
tokenizer = AutoTokenizer.from_pretrained(MODEL)
model = AutoModelForCausalLM.from_pretrained(
MODEL,
torch_dtype=torch.bfloat16,
device_map="auto",
quantization_config=quantization_config,
)
@spaces.GPU()
def stream_chat(
message: str,
history: list,
system_prompt: str,
temperature: float = 0.8,
max_new_tokens: int = 1024,
top_p: float = 1.0,
top_k: int = 20,
penalty: float = 1.2,
):
print(f"message: {message}")
print(f"history: {history}")
conversation = [{"role": "system", "content": system_prompt}]
for prompt, answer in history:
conversation.extend(
[
{"role": "user", "content": prompt},
{"role": "assistant", "content": answer},
]
)
conversation.append({"role": "user", "content": message})
input_ids = tokenizer.apply_chat_template(
conversation, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
streamer = TextIteratorStreamer(
tokenizer, timeout=60.0, skip_prompt=True, skip_special_tokens=True
)
generate_kwargs = dict(
input_ids=input_ids,
max_new_tokens=max_new_tokens,
do_sample=False if temperature == 0 else True,
top_p=top_p,
top_k=top_k,
temperature=temperature,
eos_token_id=[128001, 128008, 128009],
streamer=streamer,
)
with torch.no_grad():
thread = Thread(target=model.generate, kwargs=generate_kwargs)
thread.start()
buffer = ""
for new_text in streamer:
buffer += new_text
yield buffer
"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(
stream_chat,
additional_inputs=[
gr.Textbox(value="You are an ARM Assembly language decoder. You receive a line of Arm assembly and respond with a description of what the instruction does.", label="System message"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.95,
step=0.05,
label="Top-p (nucleus sampling)",
),
],
)
if __name__ == "__main__":
demo.launch()
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