mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-11-06 19:02:17 +08:00
96 lines
3.3 KiB
Python
96 lines
3.3 KiB
Python
# coding=utf-8
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# Implements user interface in browser for fine-tuned models.
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# Usage: python web_demo.py --model_name_or_path path_to_model --checkpoint_dir path_to_checkpoint
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import gradio as gr
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from threading import Thread
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from transformers import TextIteratorStreamer
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from transformers.utils.versions import require_version
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from llmtuner import Template, get_infer_args, load_model_and_tokenizer, get_logits_processor
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require_version("gradio>=3.30.0", "To fix: pip install gradio>=3.30.0")
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model_args, data_args, finetuning_args, generating_args = get_infer_args()
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model, tokenizer = load_model_and_tokenizer(model_args, finetuning_args)
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prompt_template = Template(data_args.prompt_template)
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source_prefix = data_args.source_prefix if data_args.source_prefix else ""
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def predict(query, chatbot, max_new_tokens, top_p, temperature, history):
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chatbot.append((query, ""))
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input_ids = tokenizer([prompt_template.get_prompt(query, history, source_prefix)], return_tensors="pt")["input_ids"]
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input_ids = input_ids.to(model.device)
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streamer = TextIteratorStreamer(tokenizer, timeout=60.0, skip_prompt=True, skip_special_tokens=True)
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gen_kwargs = generating_args.to_dict()
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gen_kwargs.update({
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"input_ids": input_ids,
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"top_p": top_p,
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"temperature": temperature,
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"max_new_tokens": max_new_tokens,
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"logits_processor": get_logits_processor(),
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"streamer": streamer
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})
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thread = Thread(target=model.generate, kwargs=gen_kwargs)
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thread.start()
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response = ""
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for new_text in streamer:
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response += new_text
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new_history = history + [(query, response)]
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chatbot[-1] = (query, response)
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yield chatbot, new_history
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def reset_user_input():
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return gr.update(value="")
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def reset_state():
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return [], []
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with gr.Blocks() as demo:
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gr.HTML("""
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<h1 align="center">
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<a href="https://github.com/hiyouga/LLaMA-Efficient-Tuning" target="_blank">
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LLaMA Efficient Tuning
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</a>
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</h1>
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""")
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chatbot = gr.Chatbot()
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with gr.Row():
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with gr.Column(scale=4):
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with gr.Column(scale=12):
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user_input = gr.Textbox(show_label=False, placeholder="Input...", lines=10).style(container=False)
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with gr.Column(min_width=32, scale=1):
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submitBtn = gr.Button("Submit", variant="primary")
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with gr.Column(scale=1):
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emptyBtn = gr.Button("Clear History")
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max_new_tokens = gr.Slider(10, 2048, value=generating_args.max_new_tokens, step=1.0,
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label="Maximum new tokens", interactive=True)
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top_p = gr.Slider(0.01, 1, value=generating_args.top_p, step=0.01,
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label="Top P", interactive=True)
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temperature = gr.Slider(0.01, 1.5, value=generating_args.temperature, step=0.01,
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label="Temperature", interactive=True)
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history = gr.State([])
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submitBtn.click(predict, [user_input, chatbot, max_new_tokens, top_p, temperature, history], [chatbot, history], show_progress=True)
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submitBtn.click(reset_user_input, [], [user_input])
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emptyBtn.click(reset_state, outputs=[chatbot, history], show_progress=True)
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demo.queue().launch(server_name="0.0.0.0", share=True, inbrowser=True)
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