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| import gradio as gr | |
| from llmlingua import PromptCompressor | |
| llm_lingua = PromptCompressor("lgaalves/gpt2-dolly", device_map="cpu") | |
| INTRO = """ | |
| # LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models (EMNLP 2023) [[paper](https://arxiv.org/abs/2310.05736)] | |
| _Huiqiang Jiang, Qianhui Wu, Chin-Yew Lin, Yuqing Yang and Lili Qiu_ | |
| ### This is an <b>early demo</b> of the prompt compression method LLMLingua and <b>the capabilities are limited</b>, restricted to using only the GPT-2 small size mode. | |
| It should be noted that due to limited resources, we only provide the **GPT2-Small** size language model in this demo. Using the **LLaMA2-7B** as a small language model would result in a significant performance improvement, especially at high compression ratios. | |
| To use it, upload your prompt and set the compression target. | |
| 1. ✅ Set the different components of the prompt separately, including instruction, context, and question. Leave the corresponding field empty if a particular component does not exist. | |
| - Question: This refers to the directives given by the user to the LLMs, such as inquiries, questions, or requests. Positioned after the instruction and context modules, the question module has a high sensitivity to compression. | |
| - Context: This module provides the supplementary context needed to address the question, such as documents, demonstrations, web search results, or API call results. Located between the instruction and question modules, its sensitivity to compression is relatively low. | |
| - Instruction: This module consists of directives given by the user to the LLMs, such as task descriptions. Placed before the instruction and context modules, the instruction module exhibits a high sensitivity to compression. | |
| 2. ✅ Set the target_token or compression ratio. | |
| 3. 🤔 Try experimenting with different target compression ratios or other hyperparameters to optimize the performance. | |
| You can check our [project page](https://llmlingua.com/)! | |
| We also has a work to compress long context scenories, using less cost but even improve the downstream performance, LongLLMLingua.<br> | |
| [LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression](https://arxiv.org/abs/2310.06839) (Under Review).<br> | |
| ## News | |
| - 👾 LLMLingua has been integrated into [LangChain](https://github.com/langchain-ai/langchain/blob/master/docs/docs/integrations/retrievers/llmlingua.ipynb) and [LlamaIndex](https://github.com/run-llama/llama_index/blob/main/docs/examples/node_postprocessor/LongLLMLingua.ipynb), two widely-used RAG frameworks. | |
| - 🤳 Talk slides are available in [AI Time Jan, 24](https://drive.google.com/file/d/1fzK3wOvy2boF7XzaYuq2bQ3jFeP1WMk3/view?usp=sharing). | |
| - 🖥 EMNLP'23 slides are available in [Session 5](https://drive.google.com/file/d/1GxQLAEN8bBB2yiEdQdW4UKoJzZc0es9t/view) and [BoF-6](https://drive.google.com/file/d/1LJBUfJrKxbpdkwo13SgPOqugk-UjLVIF/view). | |
| - 📚 Check out our new [blog post](https://medium.com/@iofu728/longllmlingua-bye-bye-to-middle-loss-and-save-on-your-rag-costs-via-prompt-compression-54b559b9ddf7) discussing RAG benefits and cost savings through prompt compression. See the script example [here](https://github.com/microsoft/LLMLingua/blob/main/examples/Retrieval.ipynb). | |
| - 🎈 Visit our [project page](https://llmlingua.com/) for real-world case studies in RAG, Online Meetings, CoT, and Code. | |
| - 👨🦯 Explore our ['./examples'](https://github.com/microsoft/LLMLingua/blob/main/examples) directory for practical applications, including [RAG](https://github.com/microsoft/LLMLingua/blob/main/examples/RAG.ipynb), [Online Meeting](https://github.com/microsoft/LLMLingua/blob/main/examples/OnlineMeeting.ipynb), [CoT](https://github.com/microsoft/LLMLingua/blob/main/examples/CoT.ipynb), [Code](https://github.com/microsoft/LLMLingua/blob/main/examples/Code.ipynb), and [RAG using LlamaIndex](https://github.com/microsoft/LLMLingua/blob/main/examples/RAGLlamaIndex.ipynb). | |
| """ | |
| INTRO_EXAMPLES = ''' | |
| ## Examples in GSM8K | |
| Below are some examples of compressing prompts in GSM8K using different small language models. The original prompt [1] is taken from "Complexity-Based Prompting for Multi-step Reasoning" [2], with an original length of 2,365 tokens. Black-box LLMs use GPT-3.5-Turbo-0301 with greedy decoding. | |
| [1] https://github.com/FranxYao/chain-of-thought-hub/blob/main/gsm8k/lib_prompt/prompt_hardest.txt<br> | |
| [2] Fu, Yao, et al. "Complexity-Based Prompting for Multi-step Reasoning." The Eleventh International Conference on Learning Representations. 2022. | |
| ''' | |
| custom_css = """ | |
| #image-upload { | |
| flex-grow: 1; | |
| } | |
| #params .tabs { | |
| display: flex; | |
| flex-direction: column; | |
| flex-grow: 1; | |
| } | |
| #params .tabitem[style="display: block;"] { | |
| flex-grow: 1; | |
| display: flex !important; | |
| } | |
| #params .gap { | |
| flex-grow: 1; | |
| } | |
| #params .form { | |
| flex-grow: 1 !important; | |
| } | |
| #params .form > :last-child{ | |
| flex-grow: 1; | |
| } | |
| .md ol, .md ul { | |
| margin-left: 1rem; | |
| } | |
| .md img { | |
| margin-bottom: 1rem; | |
| } | |
| """ | |
| EXAMPLES = [ | |
| [ | |
| "lgaalves/gpt2-dolly", | |
| "8.7x", | |
| "78.24", | |
| "Question: can buy 4 1melon for You bought 36 fruits evenly split between of 1 $. does cost if bill $\n's think step\nIf between 3 then I 363 = 12 of fruit 1 orange then oranges506If I oranges I $66 $60 on the 2 fruit\n the of is, and that price and is 1W4AIf we know we bought 12 and 12W\n thatW can the 12 = 48\n of apple (60/The 1\n: Sam a dozen boxes with 30ighter pens each Heanged into six3 the separately of three. much in\n's boxes $120 12 =Sam then took 5 boxes × 6 highlighters/box = 30 highlighters.\nHe sold these boxes for 5 * $3 = $15\nAfter selling these 5 boxes there were 360 - 30 = 330 highlighters remaining.\nThese form 330 / 3 = 110 groups of three pens.\nHe sold each of these groups for $2 each, so made 110 * 2 = $220 from them.\nIn total, then, he earned $220 + $15 = $235.\nSince his original cost was $120, he earned $235 - $120 = $115 in profit.\nThe answer is 115", | |
| ], | |
| [ | |
| "vicgalle/alpaca-7b", | |
| "13.8x", | |
| "78.32", | |
| "Question: Sam bought a dozen boxes, each 30 highl pens inside, $10 each. He reanged five of boxes into of sixlters each sold $3. He sold the theters separately at the of three $2. How much did make in total, in\nLets think step\nSam bought boxes x0 = $10 oflters.\nHe 2 300ters in\nSam then 5 boxes 6ters0ters\nHe sold these boxes for 55\nAfterelling these boxes there300lters remaining\nThese form 330 310 of three pens\nHe sold each of these groups for2 each, so made 0 *0 from\nIn total, he $ $155\nSince his original $1, he earned $20 = $115 in profit.\nThe answer is 115\n\n", | |
| ], | |
| [ | |
| "vicgalle/alpaca-7b", | |
| "20.2x", | |
| "77.94", | |
| "Question: Sam bought a dozen boxes, each with 30 highl pens inside, for $10 each.\nHe reanged five of boxes into of sixlters each sold them $3 per package.\nHe sold the rest of thelters separately at the of three pens for $2.\nHow much profit did make in total, in dollars\nLet's think step by step\nSam then took 5 boxes × 6lighters/box = 30 highlighters.\nThese form 330 / 3 = 110 groups of three pens.\nThe answer is 115\n\n", | |
| ], | |
| ] | |
| def compress_prompt(context, instruction, question, ratio, target_token): | |
| context, instruction, question = context.replace("\\n", "\n"), instruction.replace("\\n", "\n"), question.replace("\\n", "\n") | |
| compressed_prompt = llm_lingua.compress_prompt(context.split("\n\n"), instruction, question, float(ratio), float(target_token)) | |
| return [compressed_prompt[key] for key in ["compressed_prompt", "origin_tokens", "compressed_tokens", "ratio", "saving"]] | |
| with gr.Blocks(css=custom_css) as iface: | |
| gr.Markdown(INTRO) | |
| with gr.Row(): | |
| with gr.Column(elem_id="prompt", scale=2): | |
| with gr.Tab('Prompts'): | |
| instruction = gr.Textbox( | |
| label="Instruction", | |
| lines=1, | |
| value="Please reference the following examples to answer the math question,", | |
| placeholder="This module consists of directives given by the user to the LLMs, such as task descriptions.", | |
| ) | |
| context = gr.Textbox( | |
| label="Context", | |
| lines=3, | |
| value="Question: Angelo and Melanie want to plan how many hours over the next week they should study together for their test next week. They have 2 chapters of their textbook to study and 4 worksheets to memorize. They figure out that they should dedicate 3 hours to each chapter of their textbook and 1.5 hours for each worksheet. If they plan to study no more than 4 hours each day, how many days should they plan to study total over the next week if they take a 10-minute break every hour, include 3 10-minute snack breaks each day, and 30 minutes for lunch each day?\nLet's think step by step\nAngelo and Melanie think they should dedicate 3 hours to each of the 2 chapters, 3 hours x 2 chapters = 6 hours total.\nFor the worksheets they plan to dedicate 1.5 hours for each worksheet, 1.5 hours x 4 worksheets = 6 hours total.\nAngelo and Melanie need to start with planning 12 hours to study, at 4 hours a day, 12 / 4 = 3 days.\nHowever, they need to include time for breaks and lunch. Every hour they want to include a 10-minute break, so 12 total hours x 10 minutes = 120 extra minutes for breaks.\nThey also want to include 3 10-minute snack breaks, 3 x 10 minutes = 30 minutes.\nAnd they want to include 30 minutes for lunch each day, so 120 minutes for breaks + 30 minutes for snack breaks + 30 minutes for lunch = 180 minutes, or 180 / 60 minutes per hour = 3 extra hours.\nSo Angelo and Melanie want to plan 12 hours to study + 3 hours of breaks = 15 hours total.\nThey want to study no more than 4 hours each day, 15 hours / 4 hours each day = 3.75\nThey will need to plan to study 4 days to allow for all the time they need.\nThe answer is 4\n\nQuestion: Mark's basketball team scores 25 2 pointers, 8 3 pointers and 10 free throws. Their opponents score double the 2 pointers but half the 3 pointers and free throws. What's the total number of points scored by both teams added together?\nLet's think step by step\nMark's team scores 25 2 pointers, meaning they scored 25*2= 50 points in 2 pointers.\nHis team also scores 6 3 pointers, meaning they scored 8*3= 24 points in 3 pointers\nThey scored 10 free throws, and free throws count as one point so they scored 10*1=10 points in free throws.\nAll together his team scored 50+24+10= 84 points\nMark's opponents scored double his team's number of 2 pointers, meaning they scored 50*2=100 points in 2 pointers.\nHis opponents scored half his team's number of 3 pointers, meaning they scored 24/2= 12 points in 3 pointers.\nThey also scored half Mark's team's points in free throws, meaning they scored 10/2=5 points in free throws.\nAll together Mark's opponents scored 100+12+5=117 points\nThe total score for the game is both team's scores added together, so it is 84+117=201 points\nThe answer is 201\n\nQuestion: Bella has two times as many marbles as frisbees. She also has 20 more frisbees than deck cards. If she buys 2/5 times more of each item, what would be the total number of the items she will have if she currently has 60 marbles?\nLet's think step by step\nWhen Bella buys 2/5 times more marbles, she'll have increased the number of marbles by 2/5*60 = 24\nThe total number of marbles she'll have is 60+24 = 84\nIf Bella currently has 60 marbles, and she has two times as many marbles as frisbees, she has 60/2 = 30 frisbees.\nIf Bella buys 2/5 times more frisbees, she'll have 2/5*30 = 12 more frisbees.\nThe total number of frisbees she'll have will increase to 30+12 = 42\nBella also has 20 more frisbees than deck cards, meaning she has 30-20 = 10 deck cards\nIf she buys 2/5 times more deck cards, she'll have 2/5*10 = 4 more deck cards.\nThe total number of deck cards she'll have is 10+4 = 14\nTogether, Bella will have a total of 14+42+84 = 140 items\nThe answer is 140\n\nQuestion: A group of 4 fruit baskets contains 9 apples, 15 oranges, and 14 bananas in the first three baskets and 2 less of each fruit in the fourth basket. How many fruits are there?\nLet's think step by step\nFor the first three baskets, the number of apples and oranges in one basket is 9+15=24\nIn total, together with bananas, the number of fruits in one basket is 24+14=38 for the first three baskets.\nSince there are three baskets each having 38 fruits, there are 3*38=114 fruits in the first three baskets.\nThe number of apples in the fourth basket is 9-2=7\nThere are also 15-2=13 oranges in the fourth basket\nThe combined number of oranges and apples in the fourth basket is 13+7=20\nThe fourth basket also contains 14-2=12 bananas.\nIn total, the fourth basket has 20+12=32 fruits.\nThe four baskets together have 32+114=146 fruits.\nThe answer is 146\n\nQuestion: You can buy 4 apples or 1 watermelon for the same price. You bought 36 fruits evenly split between oranges, apples and watermelons, and the price of 1 orange is $0.50. How much does 1 apple cost if your total bill was $66?\nLet's think step by step\nIf 36 fruits were evenly split between 3 types of fruits, then I bought 36/3 = 12 units of each fruit\nIf 1 orange costs $0.50 then 12 oranges will cost $0.50 * 12 = $6\nIf my total bill was $66 and I spent $6 on oranges then I spent $66 - $6 = $60 on the other 2 fruit types.\nAssuming the price of watermelon is W, and knowing that you can buy 4 apples for the same price and that the price of one apple is A, then 1W=4A\nIf we know we bought 12 watermelons and 12 apples for $60, then we know that $60 = 12W + 12A\nKnowing that 1W=4A, then we can convert the above to $60 = 12(4A) + 12A\n$60 = 48A + 12A\n$60 = 60A\nThen we know the price of one apple (A) is $60/60= $1\nThe answer is 1\n\nQuestion: Susy goes to a large school with 800 students, while Sarah goes to a smaller school with only 300 students. At the start of the school year, Susy had 100 social media followers. She gained 40 new followers in the first week of the school year, half that in the second week, and half of that in the third week. Sarah only had 50 social media followers at the start of the year, but she gained 90 new followers the first week, a third of that in the second week, and a third of that in the third week. After three weeks, how many social media followers did the girl with the most total followers have?\nLet's think step by step\nAfter one week, Susy has 100+40 = 140 followers.\nIn the second week, Susy gains 40/2 = 20 new followers.\nIn the third week, Susy gains 20/2 = 10 new followers.\nIn total, Susy finishes the three weeks with 140+20+10 = 170 total followers.\nAfter one week, Sarah has 50+90 = 140 followers.\nAfter the second week, Sarah gains 90/3 = 30 followers.\nAfter the third week, Sarah gains 30/3 = 10 followers.\nSo, Sarah finishes the three weeks with 140+30+10 = 180 total followers.\nThus, Sarah is the girl with the most total followers with a total of 180.\nThe answer is 180\n\nQuestion: Sam bought a dozen boxes, each with 30 highlighter pens inside, for $10 each box. He rearranged five of these boxes into packages of six highlighters each and sold them for $3 per package. He sold the rest of the highlighters separately at the rate of three pens for $2. How much profit did he make in total, in dollars?\nLet's think step by step\nSam bought 12 boxes x $10 = $120 worth of highlighters.\nHe bought 12 * 30 = 360 highlighters in total.\nSam then took 5 boxes × 6 highlighters/box = 30 highlighters.\nHe sold these boxes for 5 * $3 = $15\nAfter selling these 5 boxes there were 360 - 30 = 330 highlighters remaining.\nThese form 330 / 3 = 110 groups of three pens.\nHe sold each of these groups for $2 each, so made 110 * 2 = $220 from them.\nIn total, then, he earned $220 + $15 = $235.\nSince his original cost was $120, he earned $235 - $120 = $115 in profit.\nThe answer is 115\n\nQuestion: In a certain school, 2/3 of the male students like to play basketball, but only 1/5 of the female students like to play basketball. What percent of the population of the school do not like to play basketball if the ratio of the male to female students is 3:2 and there are 1000 students?\nLet's think step by step\nThe students are divided into 3 + 2 = 5 parts where 3 parts are for males and 2 parts are for females.\nEach part represents 1000/5 = 200 students.\nSo, there are 3 x 200 = 600 males.\nAnd there are 2 x 200 = 400 females.\nHence, 600 x 2/3 = 400 males play basketball.\nAnd 400 x 1/5 = 80 females play basketball.\nA total of 400 + 80 = 480 students play basketball.\nTherefore, 1000 - 480 = 520 do not like to play basketball.\nThe percentage of the school that do not like to play basketball is 520/1000 * 100 = 52\nThe answer is 52\n", | |
| placeholder="This module provides the supplementary context needed to address the question, such as documents, demonstrations, web search results, or API call results.", | |
| ) | |
| question = gr.Textbox( | |
| label="Question", | |
| lines=1, | |
| value="Question: Josh decides to try flipping a house. He buys a house for $80,000 and then puts in $50,000 in repairs. This increased the value of the house by 150%. How much profit did he make?", | |
| placeholder="This refers to the directives given by the user to the LLMs, such as inquiries, questions, or requests.", | |
| ) | |
| with gr.Column(elem_id="params", scale=1): | |
| with gr.Tab('Compression Target'): | |
| target_token = gr.Textbox( | |
| label="Target Token (To use this, set Compression Ratio to 0)", | |
| value=200, | |
| ) | |
| ratio = gr.Textbox( | |
| label="Compression Ratio (To use this, set Target Token to -1)", | |
| value=0, | |
| ) | |
| gen_button = gr.Button(value="Compress Prompt!", variant="primary") | |
| with gr.Row(): | |
| with gr.Column(elem_id="Results", scale=1): | |
| with gr.Tab('Compressed Prompts'): | |
| compressed_prompt = gr.Textbox( | |
| label="compressed_prompt", | |
| lines=10, | |
| ) | |
| with gr.Column(elem_id="Results_2", scale=1): | |
| with gr.Tab('Saving'): | |
| origin_tokens = gr.Textbox( | |
| label="The tokens number of original prompt", | |
| ) | |
| compressed_tokens = gr.Textbox( | |
| label="The tokens number of compressed prompt", | |
| ) | |
| saving_ratio = gr.Textbox( | |
| label="Actual Compression Ratio", | |
| ) | |
| saving = gr.Textbox( | |
| label="Saving Cost", | |
| ) | |
| # gr.Examples( | |
| # examples=EXAMPLES, | |
| # inputs=[model_name, compressed_prompt, saving_ratio, acc], | |
| # ) | |
| gr.Markdown(INTRO_EXAMPLES) | |
| gr.Dataframe( | |
| value=EXAMPLES, | |
| headers=["Small Language Model", "Compression Ratio", "GSM8K Acc using GPT-3.5-Turbo", "Compressed Prompts",], | |
| datatype=["str", "str", "str", "str"], | |
| ), | |
| gen_button.click( | |
| fn=compress_prompt, | |
| inputs=[ | |
| context, | |
| instruction, | |
| question, | |
| ratio, | |
| target_token | |
| ], | |
| outputs=[ | |
| compressed_prompt, | |
| origin_tokens, | |
| compressed_tokens, | |
| saving_ratio, | |
| saving | |
| ], | |
| ) | |
| iface.queue(max_size=10, api_open=False).launch(show_api=False) |