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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(): | |
| submit_btn = gr.Button("🚀 Submit") | |
| empty_btn = gr.Button("🧹 Clear History") | |
| 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], queue=False) #No queue for resetting | |
| empty_btn.click(reset_state, [chatbot, task_history], outputs=[chatbot], show_progress=True, queue=False) #No queue for clearing | |
| 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) | |