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Running
on
Zero
| import threading | |
| import logging | |
| from pathlib import Path | |
| from typing import Dict | |
| import spaces | |
| import pandas as pd | |
| import gradio as gr | |
| from gradio_toggle import Toggle | |
| from transformers import TextIteratorStreamer | |
| from model import load_model | |
| from scheduler import load_scheduler | |
| from schemas import UserRequest, SteeringOutput, CONFIG | |
| logging.basicConfig(level=logging.INFO, format='%(asctime)s %(name)s %(levelname)s:%(message)s') | |
| logger = logging.getLogger(__name__) | |
| model_name = "DeepSeek-R1-Distill-Qwen-7B" | |
| examples = pd.read_csv("assets/examples.csv") | |
| instances = {} | |
| scheduler = load_scheduler() | |
| model = load_model() | |
| HEAD = """ | |
| <script src="https://cdn.jsdelivr.net/npm/bootstrap@5.3.3/dist/js/bootstrap.bundle.min.js" integrity="sha384-YvpcrYf0tY3lHB60NNkmXc5s9fDVZLESaAA55NDzOxhy9GkcIdslK1eN7N6jIeHz" crossorigin="anonymous"></script> | |
| <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.7.2/css/all.min.css" integrity="sha512-Evv84Mr4kqVGRNSgIGL/F/aIDqQb7xQ2vcrdIwxfjThSH8CSR7PBEakCr51Ck+w+/U6swU2Im1vVX0SVk9ABhg==" crossorigin="anonymous" referrerpolicy="no-referrer" /> | |
| """ | |
| HTML = f""" | |
| <div id="banner"> | |
| <h1><img src="/gradio_api/file=assets/rudder_3094973.png"> LLM Censorship Steering</h1> | |
| <div id="links" class="row" style="margin-bottom: .8em;"> | |
| <i class="fa-solid fa-file-pdf fa-lg"></i><a href="https://arxiv.org/abs/2504.17130"> Paper</a> | |
| <i class="fa-solid fa-blog fa-lg"></i><a href="https://hannahxchen.github.io/blog/2025/censorship-steering"> Blog Post</a> | |
| <i class="fa-brands fa-github fa-lg"></i><a href="https://github.com/hannahxchen/llm-censorship-steering"> Code</a> | |
| </div> | |
| <div id="cover"> | |
| <img src="/gradio_api/file=assets/demo-cover.png"> | |
| </div> | |
| </div> | |
| """ | |
| CSS = """ | |
| div.gradio-container .app { | |
| max-width: 1600px !important; | |
| } | |
| div#banner { | |
| display: flex; | |
| flex-direction: column; | |
| align-items: center; | |
| justify-content: center; | |
| h1 { | |
| font-size: 32px; | |
| line-height: 1.35em; | |
| margin-bottom: 0em; | |
| display: flex; | |
| img { | |
| display: inline; | |
| height: 1.35em; | |
| } | |
| } | |
| div#cover img { | |
| max-height: 130px; | |
| padding-top: 0.5em; | |
| } | |
| } | |
| @media (max-width: 500px) { | |
| div#banner { | |
| h1 { | |
| font-size: 22px; | |
| } | |
| div#links { | |
| font-size: 14px; | |
| } | |
| } | |
| div#model-state p { | |
| font-size: 14px; | |
| } | |
| } | |
| div#steering-toggle { | |
| padding-top: 8px; | |
| padding-bottom: 8px; | |
| .toggle-label { | |
| color: var(--body-text-color); | |
| } | |
| span p { | |
| font-size: var(--block-info-text-size); | |
| line-height: var(--line-sm); | |
| color: var(--block-label-text-color); | |
| } | |
| } | |
| div#coeff-slider { | |
| padding-bottom: 5px; | |
| .slider_input_container span {color: var(--body-text-color);} | |
| .slider_input_container { | |
| display: flex; | |
| flex-wrap: wrap; | |
| input {appearance: auto;} | |
| } | |
| } | |
| div#coeff-slider .wrap .head { | |
| justify-content: unset; | |
| label {margin-right: var(--size-2);} | |
| label span { | |
| color: var(--body-text-color); | |
| margin-bottom: 0; | |
| } | |
| } | |
| .tooltip { | |
| word-wrap: break-word; | |
| width: 12rem; | |
| } | |
| .tooltip-inner { | |
| filter: alpha(opacity=100); | |
| font-size: var(--block-info-text-size); | |
| text-align: center; | |
| padding: .4rem .2rem; | |
| background-color: var(--neutral-500); | |
| border-width: 1px; | |
| border-radius: var(--block-radius); | |
| } | |
| """ | |
| slider_info = """\ | |
| <div style='display: flex; justify-content: space-between; line-height: normal;'>\ | |
| <span style='font-size: var(--block-info-text-size); color: var(--block-label-text-color);'>Less censorship</span>\ | |
| <span style='font-size: var(--block-info-text-size); color: var(--block-label-text-color);'>More censorship</span>\ | |
| </div>\ | |
| """\ | |
| slider_ticks = """\ | |
| <datalist id='values' style='display: flex; justify-content: space-between; width: 100%; padding: 0 6px;'>\ | |
| <option value='-2' style='font-size: 13px; line-height: var(--spacing-xs); width: 1px; display: flex; justify-content: center;'>-2</option>\ | |
| <option value='-1' style='font-size: 13px; line-height: var(--spacing-xs); width: 1px; display: flex; justify-content: center;'>-1</option>\ | |
| <option value='0' style='font-size: 13px; line-height: var(--spacing-xs); width: 1px; display: flex; justify-content: center;'>0</option>\ | |
| <option value='1' style='font-size: 13px; line-height: var(--spacing-xs); width: 1px; display: flex; justify-content: center;'>1</option>\ | |
| <option value='2' style='font-size: 13px; line-height: var(--spacing-xs); width: 1px; display: flex; justify-content: center;'>2</option>\ | |
| </datalist>\ | |
| """ | |
| coeff_info = """\ | |
| <href='#' id='coeff-info' data-bs-toggle='tooltip' style='padding-left: 3px;' data-bs-html='true' data-bs-trigger='hover focus' data-bs-placement='right' data-bs-html='true' title='Recommended range is -1.5~1.5 (Outputs may be unexpected outside this range)'><i class='fa-solid fa-circle-question'></i></span>\ | |
| """ | |
| JS = """ | |
| async() => { | |
| const node = document.querySelector("div.slider_input_container"); | |
| node.insertAdjacentHTML('beforebegin', "%s"); | |
| const sliderNode = document.querySelector("input#range_id_0"); | |
| sliderNode.insertAdjacentHTML('afterend', "%s"); | |
| sliderNode.setAttribute("list", "values"); | |
| const coeffBox = document.querySelector("div#coeff-slider label span"); | |
| coeffBox.insertAdjacentHTML('afterend', "%s"); | |
| var tooltipTriggerList = [].slice.call(document.querySelectorAll('[data-bs-toggle="tooltip"]')) | |
| var tooltipList = tooltipTriggerList.map(function (tooltipTriggerEl) { | |
| return new bootstrap.Tooltip(tooltipTriggerEl) | |
| }) | |
| document.querySelector('span.min_value').remove(); | |
| document.querySelector('span.max_value').remove(); | |
| } | |
| """ % (slider_info, slider_ticks, coeff_info) | |
| def initialize_instance(request: gr.Request): | |
| instances[request.session_hash] = [] | |
| logger.info("Number of connections: %d", len(instances)) | |
| return request.session_hash | |
| def cleanup_instance(request: gr.Request): | |
| session_id = request.session_hash | |
| if session_id in instances: | |
| for data in instances[session_id]: | |
| if isinstance(data, SteeringOutput): | |
| scheduler.append(data.model_dump()) | |
| del instances[session_id] | |
| logger.info("Number of connections: %d", len(instances)) | |
| def generate(prompt: str, steering: bool, coeff: float, generation_config: Dict[str, float], layer: int, k: float): | |
| formatted_prompt = model.apply_chat_template(prompt) | |
| inputs = model.tokenize(formatted_prompt) | |
| streamer = TextIteratorStreamer(model.tokenizer, timeout=10, skip_prompt=True, skip_special_tokens=True) | |
| if steering: | |
| thread = threading.Thread( | |
| target=model.steer_generation, | |
| args=(inputs, streamer, k, layer, coeff, generation_config) | |
| ) | |
| else: | |
| thread = threading.Thread( | |
| target=model.run_generation, | |
| args=(inputs, streamer, generation_config) | |
| ) | |
| thread.start() | |
| generated_text = "<think>" | |
| for new_text in streamer: | |
| generated_text += new_text | |
| yield generated_text | |
| def generate_output( | |
| session_id: str, prompt: str, steering: bool, coeff: float, | |
| max_new_tokens: int, top_p: float, temperature: float, layer: int, vec_scaling: float | |
| ): | |
| req = UserRequest( | |
| session_id=session_id, prompt=prompt, steering=steering, coeff=coeff, | |
| max_new_tokens=max_new_tokens, top_p=top_p, temperature=temperature, vec_scale=vec_scaling, layer=layer | |
| ) | |
| logger.info("User request: %s", req) | |
| instances[session_id].append(req) | |
| yield from generate(prompt, steering, coeff, req.generation_config(), layer, req.k) | |
| async def post_process(session_id, output): | |
| req = instances[session_id].pop() | |
| if "</think>" in output: | |
| p = [p for p in output.partition("</think>") if p != ""] | |
| reasoning = "".join(p[:-1]) | |
| if len(p) == 1: | |
| answer = None | |
| else: | |
| answer = p[-1] | |
| steering_output = SteeringOutput(**req.model_dump(), reasoning=reasoning, answer=answer) | |
| instances[session_id].append(steering_output) | |
| return gr.update(interactive=True), gr.update(interactive=True) | |
| async def output_feedback(session_id, feedback): | |
| logger.info("Feedback received: %s", feedback) | |
| try: | |
| data = instances[session_id].pop() | |
| if "Upvote" in feedback: | |
| setattr(data, "upvote", True) | |
| elif "Downvote" in feedback: | |
| setattr(data, "upvote", False) | |
| instances[session_id].append(data) | |
| gr.Info("Thank you for your feedback!") | |
| except: | |
| logger.debug("Feedback submission error") | |
| gr.set_static_paths(paths=[Path.cwd().absolute() / "assets"]) | |
| theme = gr.themes.Base(primary_hue="emerald", text_size=gr.themes.sizes.text_lg).set() | |
| with gr.Blocks(title="LLM Censorship Steering", theme=theme, head=HEAD, css=CSS, js=JS) as demo: | |
| session_id = gr.State() | |
| gr.HTML(HTML) | |
| gr.Markdown(f'🤖 {model_name}') | |
| with gr.Row(elem_id="main-components"): | |
| with gr.Column(scale=1): | |
| with gr.Row(): | |
| steer_toggle = Toggle(label="Steering", info="Turn off to generate original outputs", value=True, interactive=True, scale=2, elem_id="steering-toggle") | |
| coeff = gr.Slider(label="Coefficient", value=-1.0, minimum=-2, maximum=2, step=0.1, scale=8, show_reset_button=False, elem_id="coeff-slider") | |
| def update_toggle(toggle_value): | |
| if toggle_value is True: | |
| return gr.update(label="Steering", info="Turn off to generate original outputs"), gr.update(interactive=True) | |
| else: | |
| return gr.update(label="No Steering", info="Turn on to steer model outputs"), gr.update(interactive=False) | |
| input_text = gr.Textbox(label="Input", placeholder="Enter your prompt here...", lines=6, interactive=True) | |
| with gr.Row(): | |
| clear_btn = gr.ClearButton() | |
| generate_btn = gr.Button("Generate", variant="primary") | |
| with gr.Accordion("⚙️ Advanced Settings", open=False): | |
| with gr.Row(): | |
| temperature = gr.Slider(0, 1, step=0.1, value=CONFIG["temperature"], interactive=True, label="Temperature", scale=1) | |
| top_p = gr.Slider(0, 1, step=0.1, value=CONFIG["top_p"], interactive=True, label="Top p", scale=1) | |
| with gr.Row(): | |
| layer = gr.Slider(0, 27, step=1, value=CONFIG["layer"], interactive=True, label="Steering layer", scale=2) | |
| max_new_tokens = gr.Number(CONFIG["max_new_tokens"], minimum=10, maximum=3048, interactive=True, label="Max new tokens", scale=1) | |
| vec_scaling = gr.Number(CONFIG["vec_scale"], minimum=0, interactive=True, label="Vector scaling", scale=1) | |
| with gr.Column(scale=1): | |
| output = gr.Textbox(label="Output", lines=15, max_lines=15, interactive=False) | |
| with gr.Row(): | |
| upvote_btn = gr.Button("👍 Upvote", interactive=False) | |
| downvote_btn = gr.Button("👎 Downvote", interactive=False) | |
| gr.HTML("<p>‼️ For research purposes, we log user inputs and generated outputs. Please avoid submitting any confidential or personal information.</p>") | |
| gr.Markdown("#### Examples") | |
| gr.Examples(examples=examples[examples["type"] == "sensitive"].prompt.tolist(), inputs=input_text, label="Sensitive") | |
| gr.Examples(examples=examples[examples["type"] == "harmful"].prompt.tolist(), inputs=input_text, label="Harmful") | |
| def clear(): | |
| return gr.update(interactive=False), gr.update(interactive=False) | |
| clear_btn.add([input_text, output]) | |
| generate_btn.click( | |
| generate_output, inputs=[session_id, input_text, steer_toggle, coeff, max_new_tokens, top_p, temperature, layer, vec_scaling], outputs=output | |
| ).success( | |
| post_process, inputs=[session_id, output], outputs=[upvote_btn, downvote_btn] | |
| ) | |
| upvote_btn.click(output_feedback, inputs=[session_id, upvote_btn]) | |
| downvote_btn.click(output_feedback, inputs=[session_id, downvote_btn]) | |
| layer.change(fn=lambda x: 1, inputs=vec_scaling, outputs=vec_scaling) | |
| demo.load(initialize_instance, outputs=session_id) | |
| demo.unload(cleanup_instance) | |
| if __name__ == "__main__": | |
| demo.queue(default_concurrency_limit=5) | |
| demo.launch(debug=True) | |