ModelScope这是小助手帮我找到的，https://www.modelscope.cn/studios/dash-infer/Qwen2-7B-Instruct-DashInfer-Demo/file/view/master?fileName=di_config.json&status=1

直接修改可以吗？
能帮我看下应该怎么修改么，始终看不懂。。。
#

Copyright (c) Alibaba, Inc. and its affiliates.

@file gradio_demo_qwen_ms_studio.py

#
import os
import copy
import random
import threading
import subprocess
import gradio as gr
from typing import List, Optional, Tuple, Dict

os.system("pip uninstall -y tensorflow tensorflow-estimator tensorflow-io-gcs-filesystem")
os.environ["LANG"] = "C"
os.environ["LC_ALL"] = "C"

default_system = 'You are a helpful assistant.'

from dashinfer.helper import EngineHelper, ConfigManager

log_lock = threading.Lock()

config_file = "di_config.json"
config = ConfigManager.get_config_from_json(config_file)

def download_model(model_id, revision, source="modelscope"):
print(f"Downloading model {model_id} (revision: {revision}) from {source}")
if source == "modelscope":
from modelscope import snapshot_download
model_dir = snapshot_download(model_id, revision=revision)
elif source == "huggingface":
from huggingface_hub import snapshot_download
model_dir = snapshot_download(repo_id=model_id)
else:
raise ValueError("Unknown source")

print(f"Save model to path {model_dir}")

return model_dir

cmd = f"pip show dashinfer | grep 'Location' | cut -d ' ' -f 2"
package_location = subprocess.run(cmd,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
shell=True,
text=True)
package_location = package_location.stdout.strip()
os.environ["AS_DAEMON_PATH"] = package_location + "/dashinfer/allspark/bin"
os.environ["AS_NUMA_NUM"] = str(len(config["device_ids"]))
os.environ["AS_NUMA_OFFSET"] = str(config["device_ids"][0])

download original model

download model from modelscope

original_model = {
"source": "modelscope",
"model_id": "qwen/" + config["model_name"],
"revision": "master",
"model_path": ""
}
original_model["model_path"] = download_model(original_model["model_id"],
original_model["revision"],
original_model["source"])

engine_helper = EngineHelper(config)
engine_helper.verbose = True
engine_helper.init_tokenizer(original_model["model_path"])

convert huggingface model to dashinfer model

only one conversion is required

engine_helper.convert_model(original_model["model_path"])

engine_helper.init_engine()
engine_max_batch = engine_helper.engine_config["engine_max_batch"]

#

History = List[Tuple[str, str]]
Messages = List[Dict[str, str]]

class Role:
USER = 'user'
SYSTEM = 'system'
BOT = 'bot'
ASSISTANT = 'assistant'
ATTACHMENT = 'attachment'

def clear_session() -> History:
return '', []

def modify_system_session(system: str) -> str:
if system is None or len(system) == 0:
system = default_system
return system, system, []

def history_to_messages(history: History, system: str) -> Messages:
messages = [{'role': Role.SYSTEM, 'content': system}]
for h in history:
messages.append({'role': Role.USER, 'content': h[0]})
messages.append({'role': Role.ASSISTANT, 'content': h[1]})
return messages

def messages_to_history(messages: Messages) -> Tuple[str, History]:
assert messages[0]['role'] == Role.SYSTEM
system = messages[0]['content']
history = []
for q, r in zip(messages[1::2], messages[2::2]):
history.append([q['content'], r['content']])
return system, history

def message_to_prompt(messages: Messages) -> str:
prompt = ""
for item in messages:
im_start, im_end = "<|im_start|>", "<|im_end|>"
prompt += f"\n{im_start}{item['role']}\n{item['content']}{im_end}"
prompt += f"\n{im_start}assistant\n"
return prompt

def model_chat(query: Optional[str], history: Optional[History],
system: str) -> Tuple[str, str, History]:
if query is None:
query = ''
if history is None:
history = []

messages = history_to_messages(history, system)
messages.append({'role': Role.USER, 'content': query})
prompt = message_to_prompt(messages)

gen_cfg = copy.deepcopy(engine_helper.default_gen_cfg)
gen_cfg["seed"] = random.randint(0, 10000)

request_list = engine_helper.create_request([prompt], [gen_cfg])

request = request_list[0]
gen = engine_helper.process_one_request_stream(request)
for response in gen:
    role = Role.ASSISTANT
    system, history = messages_to_history(messages + [{'role': role, 'content': response}])
    yield '', history, system

json_str = engine_helper.convert_request_to_jsonstr(request)
log_lock.acquire()
try:
    print(f"{json_str}\n")
finally:
    log_lock.release()

#

with gr.Blocks() as demo:
demo_title = "

with gr.Row():
    with gr.Column(scale=3):
        system_input = gr.Textbox(value=default_system,
                                  lines=1,
                                  label='System')
    with gr.Column(scale=1):
        modify_system = gr.Button("🛠️ Set system prompt and clear history.", scale=2)
    system_state = gr.Textbox(value=default_system, visible=False)
chatbot = gr.Chatbot(label=config["model_name"])
textbox = gr.Textbox(lines=2, label='Input')

with gr.Row():
    clear_history = gr.Button("🧹 Clear history")
    sumbit = gr.Button("🚀 Send")

sumbit.click(model_chat,
             inputs=[textbox, chatbot, system_state],
             outputs=[textbox, chatbot, system_input],
             concurrency_limit=engine_max_batch)
clear_history.click(fn=clear_session,
                    inputs=[],
                    outputs=[textbox, chatbot],
                    concurrency_limit=engine_max_batch)
modify_system.click(fn=modify_system_session,
                    inputs=[system_input],
                    outputs=[system_state, system_input, chatbot],
                    concurrency_limit=engine_max_batch)

demo.queue(api_open=False).launch(height=800, share=False, server_name="127.0.0.1", server_port=7860)