Spaces:
Sleeping
Sleeping
File size: 4,186 Bytes
0fc60cb e4aa56d 0fc60cb e4aa56d 0fc60cb e4aa56d 8f14c60 0fc60cb 614e7ac 0fc60cb 614e7ac 0fc60cb 614e7ac 0fc60cb 614e7ac 0fc60cb |
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 |
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 = 2048
DEFAULT_MAX_NEW_TOKENS = 1024
MAX_INPUT_TOKEN_LENGTH = 4096
DESCRIPTION = """\
# ChatSDB
这是SequioaDB旗下的AI智能大语言模型，训练超过上万条真实数据和7亿参数。
ChatSDB是SequoiaDB旗下的AI智能大语言模型，训练超过上万条真实数据和7亿参数</h3>
<br><strong>模型🔗: <a>https://huggingface.co/wangzhang/ChatSDB </a></strong>
<br><strong>Dataset🔗: <a>https://huggingface.co/datasets/wangzhang/sdb </a></strong>
<br><strong> API Doc🔗: <a>https://zgg3nzdpswxy4a-80.proxy.runpod.net/docs/ <a> </strong>
"""
LICENSE = """ """
if not torch.cuda.is_available():
DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"
if torch.cuda.is_available():
model_id = "wangzhang/ChatSDB-hf"
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, 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 = 1.2,
) -> Iterator[str]:
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})
chat = tokenizer.apply_chat_template(conversation, tokenize=False)
inputs = tokenizer(chat, return_tensors="pt", add_special_tokens=False).to("cuda")
if len(inputs) > MAX_INPUT_TOKEN_LENGTH:
inputs = inputs[-MAX_INPUT_TOKEN_LENGTH:]
gr.Warning("Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.")
streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)
generate_kwargs = dict(
inputs,
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,
)
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.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=0.1,
maximum=4.0,
step=0.1,
value=0.6,
),
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=1.2,
),
],
stop_btn=None,
examples=[
["如何安装巨杉数据库SequioaDB？"],
["巨杉数据库SequioaDB有哪些优势？"],
["巨杉数据库SequioaDB是什么？"],
],
)
with gr.Blocks(css="style.css") as demo:
gr.Markdown(DESCRIPTION)
gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button")
chat_interface.render()
gr.Markdown(LICENSE)
if __name__ == "__main__":
demo.queue(max_size=20).launch() |