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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
from transformers import BitsAndBytesConfig
nf4_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
)
MAX_MAX_NEW_TOKENS = 2048
DEFAULT_MAX_NEW_TOKENS = 1024
total_count=0
MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))
DESCRIPTION="""CODE"""
model_id = "deepseek-ai/deepseek-coder-33b-instruct"
model = AutoModelForCausalLM.from_pretrained(model_id,device_map="auto",quantization_config=nf4_config)
tokenizer=AutoTokenizer.from_pretrained(model_id)
tokenizer.use_defaul_system_prompt=True
os.system("nvidia-smi")
@spaces.GPU
def gen(
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,
)->Iterator[str]:
global total_count
total_count += 1
print(total_count)
os.system("nvidia-smi")
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=False,
top_p=top_p,
top_k=top_k,
num_beams=1,
# temperature=temperature,
repetition_penalty=repetition_penalty,
eos_token_id=32021
)
t = Thread(target=model.generate, kwargs=generate_kwargs)
t.start()
outputs = []
for text in streamer:
outputs.append(text)
yield "".join(outputs).replace("<|EOT|>","")
chat_interface = gr.ChatInterface(
fn=gen,
additional_inputs=[
gr.Textbox(label="System prompt", lines=6),
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,
# maximum=4.0,
# step=0.1,
# value=0,
# ),
gr.Slider(
label="Top-p (nucleus sampling)",
minimum=0.05,
maximum=1.0,
step=0.05,
value=0.9,
),
gr.Slider(
label="Top-k",
minimum=1,
maximum=1000,
step=1,
value=50,
),
gr.Slider(
label="Repetition penalty",
minimum=1.0,
maximum=2.0,
step=0.05,
value=1,
),
],
stop_btn=gr.Button("Stop"),
examples=[
["implement snake game using pygame"],
["Can you explain briefly to me what is the Python programming language?"],
["write a program to find the factorial of a number"],
],
)
with gr.Blocks(css="style.css") as demo:
gr.Markdown(DESCRIPTION)
chat_interface.render()
if __name__ == "__main__":
demo.queue(max_size=20).launch() |