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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
MAX_MAX_NEW_TOKENS = 1024
DEFAULT_MAX_NEW_TOKENS = 512
total_count=0
MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "2048"))
DESCRIPTION = """\
# DeepSeek-1.3B-Chat
"""
if not torch.cuda.is_available():
DESCRIPTION += "\n<p>Running on CPU 🥶.</p>"
model_id = "deepseek-ai/deepseek-coder-1.3b-instruct"
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float32, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_id)
if torch.cuda.is_available():
model_id = "deepseek-ai/deepseek-coder-1.3b-instruct"
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_id)
tokenizer.use_default_system_prompt = False
#@spaces.GPU
def generate(
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)
if total_count % 50 == 0 :
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", add_generation_prompt=True)
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=generate,
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=None,
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() as demo:
gr.Markdown(DESCRIPTION)
chat_interface.render()
if __name__ == "__main__":
demo.queue(max_size=5).launch() |