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| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| import gradio as gr | |
| import mdtex2html | |
| import torch | |
| """Override Chatbot.postprocess""" | |
| model_path = 'THUDM/BPO' | |
| device = 'cuda:0' | |
| if torch.cuda.is_available(): | |
| tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True, add_prefix_space=True) | |
| model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True, device_map=device, load_in_8bit=True) | |
| model = model.eval() | |
| DESCRIPTION = """This Space demonstrates model [BPO](https://huggingface.co/THUDM/BPO), which is built on LLaMA-2-7b-chat. | |
| BPO aims to improve the alignment of LLMs with human preferences by optimizing user prompts. | |
| Feel free to play with it, or duplicate to run generations without a queue! 🔎 For more details about the BPO model, take a look [at our paper](https://arxiv.org/pdf/2311.04155.pdf). | |
| """ | |
| LICENSE = """ | |
| --- | |
| As BPO is a fine-tuned version of [Llama-2-7b-chat](https://huggingface.co/meta-llama/Llama-2-7b-chat) by Meta, | |
| this demo is governed by the original [license](https://huggingface.co/spaces/CCCCCC/BPO_demo/blob/main/LICENSE.txt) and [acceptable use policy](https://huggingface.co/spaces/CCCCCC/BPO_demo/blob/main/USE_POLICY.md). | |
| """ | |
| if not torch.cuda.is_available(): | |
| DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>" | |
| prompt_template = "[INST] You are an expert prompt engineer. Please help me improve this prompt to get a more helpful and harmless response:\n{} [/INST]" | |
| 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 | |
| def parse_text(text): | |
| """copy from https://github.com/GaiZhenbiao/ChuanhuChatGPT/""" | |
| 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("`", "\`") | |
| 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 | |
| def predict(input, chatbot, max_length, top_p, temperature, history): | |
| if input.strip() == "": | |
| chatbot = [(parse_text(input), parse_text("Please input a valid user prompt. Empty string is not supported."))] | |
| return chatbot, history | |
| prompt = prompt_template.format(input) | |
| model_inputs = tokenizer(prompt, return_tensors="pt").to(device) | |
| output = model.generate(**model_inputs, max_length=max_length, do_sample=True, top_p=top_p, | |
| temperature=temperature, num_beams=1) | |
| resp = tokenizer.decode(output[0], skip_special_tokens=True).split('[/INST]')[1].strip() | |
| optimized_prompt = """Here are several optimized prompts: | |
| ====================Stable Optimization==================== | |
| """ | |
| optimized_prompt += resp | |
| chatbot = [(parse_text(input), parse_text(optimized_prompt))] | |
| yield chatbot, history | |
| optimized_prompt += "\n\n====================Aggressive Optimization====================" | |
| texts = [input] * 5 | |
| responses = [] | |
| num = 0 | |
| for text in texts: | |
| num += 1 | |
| seed = torch.seed() | |
| torch.manual_seed(seed) | |
| prompt = prompt_template.format(text) | |
| min_length = len(tokenizer(prompt)['input_ids']) + len(tokenizer(text)['input_ids']) + 5 | |
| model_inputs = tokenizer(prompt, return_tensors="pt").to(device) | |
| bad_words_ids = [tokenizer(bad_word, add_special_tokens=False).input_ids for bad_word in ["[PROTECT]", "\n\n[PROTECT]", "[KEEP", "[INSTRUCTION]"]] | |
| # eos and \n | |
| eos_token_ids = [tokenizer.eos_token_id, 13] | |
| output = model.generate(**model_inputs, max_new_tokens=1024, do_sample=True, top_p=0.9, temperature=0.9, bad_words_ids=bad_words_ids, num_beams=1, eos_token_id=eos_token_ids, min_length=min_length) | |
| resp = tokenizer.decode(output[0], skip_special_tokens=True).split('[/INST]')[1].split('[KE')[0].split('[INS')[0].split('[PRO')[0].strip() | |
| optimized_prompt += f"\n{num}. {resp}" | |
| chatbot = [(parse_text(input), parse_text(optimized_prompt))] | |
| yield chatbot, history | |
| # return chatbot, history | |
| def reset_user_input(): | |
| return gr.update(value='') | |
| def reset_state(): | |
| return [], [] | |
| def update_textbox_from_dropdown(selected_example): | |
| return selected_example | |
| with gr.Blocks(css="sty.css") as demo: | |
| gr.HTML("""<h1 align="center">Prompt Preference Optimizer</h1>""") | |
| gr.Markdown(DESCRIPTION) | |
| gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button") | |
| chatbot = gr.Chatbot(label="Prompt Optimization Chatbot") | |
| with gr.Row(): | |
| with gr.Column(scale=4): | |
| with gr.Column(scale=12): | |
| dropdown = gr.Dropdown(["tell me about harry potter", "give me 3 tips to learn English", "write a story about love"], label="Choose an example input") | |
| user_input = gr.Textbox(show_label=False, placeholder="User Prompt...", lines=5).style( | |
| container=False) | |
| with gr.Column(min_width=32, scale=1): | |
| submitBtn = gr.Button("Submit", variant="primary") | |
| with gr.Column(scale=1): | |
| emptyBtn = gr.Button("Clear History") | |
| max_length = gr.Slider(0, 4096, value=2048, step=1.0, label="Maximum length", interactive=True) | |
| top_p = gr.Slider(0, 1, value=0.9, step=0.01, label="Top P", interactive=True) | |
| temperature = gr.Slider(0, 1, value=0.6, step=0.01, label="Temperature", interactive=True) | |
| gr.Markdown(LICENSE) | |
| dropdown.change(update_textbox_from_dropdown, dropdown, user_input) | |
| history = gr.State([]) | |
| submitBtn.click(predict, [user_input, chatbot, max_length, top_p, temperature, history], [chatbot, history], | |
| show_progress=True) | |
| submitBtn.click(reset_user_input, [], [user_input]) | |
| emptyBtn.click(reset_state, outputs=[chatbot, history], show_progress=True) | |
| demo.queue().launch(share=False, inbrowser=True) |