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import os
import gradio as gr
import mdtex2html
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
# Initialize model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen-14B-Chat-int4", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-14B-Chat-int4", device_map="auto", trust_remote_code=True).eval()
model.generation_config = GenerationConfig.from_pretrained("Qwen/Qwen-14B-Chat-int4", trust_remote_code=True)
# Postprocess function
def postprocess(self, y):
if y is None:
return []
for i, (message, response) in enumerate(y):
y[i] = (
None if message is None else mdtex2html.convert(message),
None if response is None else mdtex2html.convert(response),
)
return y
gr.Chatbot.postprocess = postprocess
# Text parsing function
def _parse_text(text):
lines = text.split("\n")
lines = [line for line in lines if line != ""]
count = 0
for i, line in enumerate(lines):
if "```" in line:
count += 1
items = line.split("`")
if count % 2 == 1:
lines[i] = f'<pre><code class="language-{items[-1]}">'
else:
lines[i] = f"<br></code></pre>"
else:
if i > 0:
if count % 2 == 1:
line = line.replace("`", r"\`")
line = line.replace("<", "<")
line = line.replace(">", ">")
line = line.replace(" ", " ")
line = line.replace("*", "*")
line = line.replace("_", "_")
line = line.replace("-", "-")
line = line.replace(".", ".")
line = line.replace("!", "!")
line = line.replace("(", "(")
line = line.replace(")", ")")
line = line.replace("$", "$")
lines[i] = "<br>" + line
text = "".join(lines)
return text
# Demo launching function
def _launch_demo(args, model, tokenizer, config):
def predict(_query, _chatbot, _task_history):
print(f"User: {_parse_text(_query)}")
_chatbot.append((_parse_text(_query), ""))
full_response = ""
for response in model.chat_stream(tokenizer, _query, history=_task_history, generation_config=config):
_chatbot[-1] = (_parse_text(_query), _parse_text(response))
yield _chatbot
full_response = _parse_text(response)
print(f"History: {_task_history}")
_task_history.append((_query, full_response))
print(f"Qwen-Chat: {_parse_text(full_response)}")
def regenerate(_chatbot, _task_history):
if not _task_history:
yield _chatbot
return
item = _task_history.pop(-1)
_chatbot.pop(-1)
yield from predict(item[0], _chatbot, _task_history)
def reset_user_input():
return gr.update(value="")
def reset_state(_chatbot, _task_history):
_task_history.clear()
_chatbot.clear()
import gc
gc.collect()
torch.cuda.empty_cache()
return _chatbot
with gr.Blocks() as demo:
gr.Markdown("""
## Qwen-14B-Chat: A Large Language Model by Alibaba Cloud
**Space created by [@artificialguybr](https://twitter.com/artificialguybr) based on QWEN Code. Thanks HF for GPU!**
**Qwen is currently SOTA in the benchmarks for 14B models.**
""")
chatbot = gr.Chatbot(label='Qwen-Chat', elem_classes="control-height", queue=True)
query = gr.Textbox(lines=2, label='Input')
task_history = gr.State([])
with gr.Row():
empty_btn = gr.Button("🧹 Clear History")
submit_btn = gr.Button("🚀 Submit")
regen_btn = gr.Button("🤔️ Regenerate")
submit_btn.click(predict, [query, chatbot, task_history], [chatbot], show_progress=True, queue=True) # Enable queue
submit_btn.click(reset_user_input, [], [query])
empty_btn.click(reset_state, [chatbot, task_history], outputs=[chatbot], show_progress=True)
regen_btn.click(regenerate, [chatbot, task_history], [chatbot], show_progress=True, queue=True) # Enable queue
gr.Markdown("""### Performance Metrics:
- **MMLU Accuracy**:
- 0-shot: 64.6
- 5-shot: 66.5
- **HumanEval Pass@1**: 43.9
- **GSM8K Accuracy**:
- 0-shot: 60.1
- 8-shot: 59.3
""")
demo.queue(max_size=20)
demo.launch()
# Main execution
if __name__ == "__main__":
_launch_demo(None, model, tokenizer, model.generation_config)
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