app.py

import spaces
import gradio as gr
from huggingface_hub import InferenceClient
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
import torch
import subprocess
import os

# os.system("pip install dashscope")

subprocess.run("rm -rf /data-nvme/zerogpu-offload/*", env={}, shell=True)
subprocess.run("pip install flash-attn --no-build-isolation", env={"FLASH_ATTENTION_SKIP_CUDA_BUILD": "TRUE"}, shell=True)

kwargs = {}

"""
https://hugging-face.cn/docs/transformers/quantization/bitsandbytes
"""

# quantization_config = BitsAndBytesConfig(
#     load_in_4bit=True,
#     bnb_4bit_quant_type="nf4",
#     bnb_4bit_use_double_quant=True,
#     bnb_4bit_compute_dtype=torch.bfloat16,
# )

# quantization_config = BitsAndBytesConfig(
#     load_in_8bit=True,
#     # llm_int8_enable_fp32_cpu_offload=True,
# )

# kwargs = { "quantization_config": quantization_config, "low_cpu_mem_usage": True }

tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct", trust_remote_code=True, torch_dtype=torch.bfloat16, **kwargs).cuda()

@spaces.GPU
def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    modelx = model

    if len(message) < 1:
        message = "write a quick sort algorithm in python."

    messages = [
        { "role": "user", "content": message }
    ]

    inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(modelx.device)

    outputs = modelx.generate(inputs, max_new_tokens=max_tokens, do_sample=True, temperature=temperature, top_k=50, top_p=top_p, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id)

    return tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)

"""
For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
"""
# client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")

# @spaces.GPU
# def respond(
#     message,
#     history: list[tuple[str, str]],
#     system_message,
#     max_tokens,
#     temperature,
#     top_p,
# ):
#     messages = [{"role": "system", "content": system_message}]

#     for val in history:
#         if val[0]:
#             messages.append({"role": "user", "content": val[0]})
#         if val[1]:
#             messages.append({"role": "assistant", "content": val[1]})

#     if len(message) < 1:
#         message = "write a quick sort algorithm in python."

#     messages.append({"role": "user", "content": message})

#     response = ""

#     for message in client.chat_completion(
#         messages,
#         max_tokens=max_tokens,
#         stream=True,
#         temperature=temperature,
#         top_p=top_p,
#     ):
#         token = message.choices[0].delta.content

#         response += token
#         yield response

"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/main/docs/gradio/chatinterface
"""

css = """
#msg_input {
    flex-grow: 7;
}
"""

demo = gr.ChatInterface(
    fn=respond,
    textbox=gr.Textbox(elem_id="msg_input", placeholder="write a quick sort algorithm in python."),
    additional_inputs=[
        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.95,
            step=0.05,
            label="Top-p (nucleus sampling)",
        ),
    ],
    css=css,
)


if __name__ == "__main__":
    demo.launch()