File size: 4,622 Bytes
f401051 3e437da f401051 3e437da f401051 3e437da f401051 3e437da f401051 3e437da f401051 3e437da f401051 3e437da f401051 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 |
import os
from threading import Thread
from typing import Iterator
import gradio as gr
import spaces
import torch
from transformers import BitsAndBytesConfig, AutoConfig, AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
MAX_MAX_NEW_TOKENS = 2048
DEFAULT_MAX_NEW_TOKENS = 1024
MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model_path = "vinai/PhoGPT-4B-Chat"
config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
config.init_device = device
quantization = BitsAndBytesConfig(load_in_8bit=True)
model = AutoModelForCausalLM.from_pretrained(model_path,
config=config,
quantization_config =quantization,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
low_cpu_mem_usage=True)
model.eval()
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
@spaces.GPU(duration=120)
def generate(
message: str,
chat_history: list[tuple[str, 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 = []
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, add_generation_prompt=True, 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=20.0, skip_prompt=True, skip_special_tokens=True)
generate_kwargs ={
"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,
"eos_token_id":tokenizer.eos_token_id,
"pad_token_id":tokenizer.pad_token_id
}
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,
chatbot=gr.Chatbot(height=500, label = "VN GPT", show_label=True),
textbox=gr.Textbox(placeholder="Nhập hội thoại tại đây", container=False, scale=7),
additional_inputs=[
gr.Slider(
label="Độ dài token",
minimum=1,
maximum=MAX_MAX_NEW_TOKENS,
step=1,
value=DEFAULT_MAX_NEW_TOKENS,
),
gr.Slider(
label="Độ sáng tạo",
minimum=0.1,
maximum=4.0,
step=0.1,
value=0.6,
),
gr.Slider(
label="Lựa chọn từ dựa trên xác suất tích lũy",
minimum=0.05,
maximum=1.0,
step=0.05,
value=0.9,
),
gr.Slider(
label="Lựa chọn k từ có xác suất cao nhất",
minimum=1,
maximum=1000,
step=1,
value=50,
),
gr.Slider(
label="Phạt lặp lại",
minimum=1.0,
maximum=2.0,
step=0.05,
value=1.2,
),
],
theme="soft",
stop_btn=None,
examples = [
["Lợi ích của sữa mẹ ?"],
["Sữa non là gì ?"],
["Trẻ sơ sinh cần ngủ bao nhiêu giờ mỗi ngày?"],
["Bao lâu nên cho trẻ sơ sinh bú một lần?"],
["Khi nào nên bắt đầu cho trẻ ăn dặm?"],
["Làm thế nào để giúp trẻ ngủ ngon vào ban đêm?"]
],
cache_examples=False,
title = "VN-GPT",
clear_btn="🗑️ Xóa",
undo_btn="↩️ Hoàn tác",
submit_btn="🚀 Gửi",
retry_btn="🔄 Thử lại",
additional_inputs_accordion="Tùy chỉnh nâng cao",
)
if __name__ == "__main__":
chat_interface.queue(max_size=20).launch() |