Trick 1: Specify The Role原本提示:「Summarize this article」(摘要這篇文章) 調整後提示:「Summarize this article like a journalist」(像個記者一樣幫我摘要這篇文章) 原本提示:「Give me feedback on my resume」(給我履歷建議) 調整後提示:「Give me feedback on my resume like a hiring manager」(像個人資主管一樣給我履歷建議) Like a teacher:用於將複雜主題拆解成簡單、循序漸進的課程。 Like a therapist:用於以冷靜、富同理心的方式處理情緒或棘手對話的策略。 Like a coach:用於設定目標、制定每週計畫,或提供激勵人心的建議。 Like a nutritionist:用於提供專注於營養均衡、價格實惠且準備簡單的每週食譜。 Like a project manager:用於以清晰好懂的優先順序、截止日期來組織任務清單。 Trick 2: Give It Context About YourselfTrick 3: Ask It to Think Step by StepThink through this step by step before giving me the final answer. Or Walk me through your reasoning before you conclude. Bad prompt: I need a raise in my current company. So please help me draft an email to my boss. Good prompt: You are an expert career coach who has helped hundreds of professionals negotiate better salaries. I’ve been at my company for two years, I’ve led three successful projects this year, and my market value is higher than what I’m currently earning. Help me strategies an email to my manager asking for a raise. Think through the best approach step by step. | 5 個 prompt 做決策、盯專案、回顧會議重點 1. 提前預知會議重點:讓 AI 幫你搞懂對方在想什麼 Prompt:「根據我與[/person]先前的互動,列出對方在我們下次會議中最可能關注的 5 件事。」(Based on my prior interactions with [/person], give me 5 things likely top of mind for our next meeting.) 2. 一鍵生成專案報告:告別繁瑣的彙整工作 Prompt:「根據郵件、聊天紀錄和[/series]中的所有會議,撰寫一份專案更新報告:包括 KPI 與目標的差距、成敗分析、風險點、競爭對手動態,以及可能被問到的尖銳問題與建議回答。」(Draft a project update based on emails, chats, and all meetings in [/series]: KPIs vs. targets, wins/losses, risks, competitive moves, plus likely tough questions and answers.) 3. 專案進度量化分析:用數據看清成功機率 Prompt:「我們是否準備好在 11 月推出[Product]?請檢查工程進度、試點計畫成果和潛在風險,並提供一個成功機率。」(Are we on track for the [Product] launch in November? Check eng progress, pilot program results, risks. Give me a probability.) 4. 時間分析:搞懂自己的時間都花去哪了 Prompt:「請分析我過去一個月的行事曆與電子郵件,整理出 5 到 7 類我花最多時間處理的專案,列出各自的時間占比與簡短說明。」(Review my calendar and email from the last month and create 5 to 7 buckets for projects I spend most time on, with % of time spent and short descriptions.) 5. 會議準備神器:自動回顧討論脈絡 Prompt:「請根據我選取的電子郵件內容,以及過去主管與團隊的討論,幫我準備接下來的[/series]會議。」(Review [/select email] + prep me for the next meeting in [/series], based on past manager and team discussions.) | 如何讓ChatGPT成為「影子寫手」,寫出專屬於你的「味道」 深津式泛用Prompt#Instructions(你想要它扮演的角色) 你是專業的編輯。根據以下規範和輸入的句子來輸出最佳摘要。 #Constraints(你想要的文章樣貌) 字符數約為300個字符。小學生也能輕鬆理解。保持句子簡潔。 指令:文章內容必須包含:A、B、C。 這裡的ABC除了可以填入你想要文章包含的元素以外,也可以填入一些你想要ChatGPT了解的背景資料,讓它能根據這些資料撰寫,而不是隨口亂編。 如果你想要讓這篇文章的架構更縝密,你可以先大致分配文章的段落和各個段落的重點,並將指令改為:請將文章分成五段,這五段的內容分別為 A、B、C、D、E,並照順序寫作。 #Input(你想要修改 / 摘錄的文字) (填入想要摘錄的文本) #output Line採用了名為CO-STAR的提示詞撰寫架構,來切分提示任務。這幾個字母分別代表不同要素,比如C是指情境(Context),也就是在提示中描述任務概況、賦予LLM角色;O則指目標(Objective),即在提示中告知LLM想實現的目標,像是「給出案件類別,並總結案件始末」。 再來是S,也就是回覆風格(Style),比如告訴LLM,要以客服身分回答問題。T則是語調(Tone),可以在提示中,要求LLM以溫柔的語調回覆。A則指受眾(Audience)目標,R是輸出的格式(Response),比如「將標籤和案件解釋區分開來」這類描述。 |
https://analyticsindiamag.com/top-5-llm-benchmarks/You don’t need hosted LLMs, do you?https://betterprogramming.pub/you-dont-need-hosted-llms-do-you-1160b2520526 https://www.vellum.ai/blog/should-i-use-prompting-rag-or-fine-tuning Afaque Umer https://ai.plainenglish.io/%EF%B8%8F-langchain-streamlit-llama-bringing-conversational-ai-to-your-local-machine-a1736252b172 https://github.com/afaqueumer/DocQA https://huggingface.co/TheBloke/LLaMa-7B-GGML 1.Visual Studio with C++ 2.https://github.com/abetlen/llama-cpp-python https://artificialcorner.com/answering-question-about-your-documents-using-langchain-and-not-openai-2f75b8d639ae https://artificialcorner.com/lamini-is-here-a-little-giant-llm-on-your-cpu-8af30ff5a7c2 https://towardsdatascience.com/distributed-llama-2-on-cpus-via-llama-cpp-pyspark-65736e9f466d https://simonwillison.net/2023/Aug/3/weird-world-of-llms/ https://huggingface.co/TheBloke/airoboros-l2-7b-gpt4-1.4.1-GGML/tree/main | 人工智慧大語言模型相關技術用於環化業務的可行性研究 先進人工智慧技術應用於電業水處理程序的可行性探討 usage:tech review, coding assissting, process alert, report QA, ViT, DETR AI, LLM, Transformer, Attention, Llama cpp python langchain Abby Morgan https://www.comet.com/site/blog/explainable-ai-for-transformers/ langchain說明: https://python.langchain.com/docs/integrations/llms/llamacpp https://ai.plainenglish.io/from-idea-to-reality-creating-llm-powered-apps-with-langchain-a0317a23590d LLM排行榜: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard 微軟導入ChatGPT和機器人互動 我們離賈維斯不遠了!https://www.cool3c.com/article/197069 https://agi-sphere.com/llama-2/ https://onpassive.com/blog/top-5-benefits-of-robotic-process-automation/ https://bdtechtalks.com/2023/08/14/llm-api-server-nocode/
| 1.https://replicate.com/blog/how-to-prompt-llama 2.https://huggingface.co/blog/stackllama(StackLLaMA: A hands-on guide to train LLaMA with RLHF) 3.https://medium.com/@mikeyoung_97230/harnessing-the-power-of-llama-v2-for-chat-applications-9b0c7597a9fa 4.https://notes.aimodels.fyi/building-a-customer-support-chatbot-with-langchain-and-deepinfra-a-step-by-step-guide/ llama cpp=ggml inference time:7b=60 sec;13b=120 sec; chat2model 1.Nous-Hermes-13b-Chinese.ggmlv3.q4_0.bin 2.llama-cpp-python 0.1.78 3.langchain 0.0.27 chat2data 1.airoboros-l2-7b-2.2.Q4_0.gguf dir1 = "./model_llm/" llm = LlamaCpp(model_path=dir1 + "airoboros-l2-7b-2.2.Q4_0.gguf", n_ctx=1024)#, n_gqa=8) 2.#embeddings = LlamaCppEmbeddings(model_path=dir1 + "airoboros-l2-13b-gpt4-1.4.1.ggmlv3.q4_0.bin")#, n_gqa=8) embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2") # Equivalent to SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2") shibing624/text2vec-base-chinese 3.llama-cpp-python 0.2.6 4.langchain 0.0.300 可以中文問,可能英文答!(中文embedding/英文llm) pip install chromadb要記得重啟kernel!!! |
https://lexica.art/ 老共的問題 1.房價不准下跌,一開始是怕地方政府的擔保品就是房子的資產會縮水 2.貨幣政策,人民幣匯率和資本的自由流通三者不可並得,為了控制人民幣匯率不至於降得太快,會用掉儲存的美元外匯,所以其終局就是外匯會用完,人民幣則是災難式的貶值 | Python https://www.freecodecamp.org/news/object-oriented-programming-python/ | 智慧化系統水鐵分析軟硬體整合應用實作研發 1.影像收集 2.模型訓練 3.硬體選用 4.程式撰寫 5.軟硬體整合 cycle water, analysis, edge computing, training, deploying |
QA: https://towardsdatascience.com/4-ways-of-question-answering-in-langchain-188c6707cc5a summarization: Named Entitieshttps://cobusgreyling.medium.com/using-a-large-language-model-for-entity-extraction-6fffb988eb15 Agent https://gathnex.medium.com/how-to-create-your-own-llm-agent-from-scratch-a-step-by-step-guide-14b763e5b3b8 LLM agents are are programs that use large language models to decide how and when to use tools to complete tasks. https://towardsdev.com/llamaindex-yet-another-powerful-framework-to-build-efficient-knowledge-bots-06065f60605f ASR, speech to text https://deepgram.com/learn/benchmarking-top-open-source-speech-models https://github.com/malceore/voice-assistant-client https://www.assemblyai.com/blog/deepspeech-for-dummies-a-tutorial-and-overview-part-1/ https://www.assemblyai.com/blog/the-state-of-python-speech-recognition-in-2021/ https://github.com/mozilla/DeepSpeech-examples/blob/r0.9/mic_vad_streaming/README.rst | whisper https://github.com/tobiashuttinger/openai-whisper-realtime/blob/main/openai-whisper-realtime.py https://medium.com/@dominique.heer/controlling-your-computer-with-voice-commands-by-using-openai-whisper pip install SpeechRecognition[whisper-local] pyaudio setuptools https://github.com/Uberi/speech_recognition/blob/master/examples/threaded_workers.py https://fahizkp.medium.com/building-a-robust-real-time-transcription-system-with-openais-whisper-6a0b40c4b997 https://pypi.org/project/RealtimeSTT/ https://github.com/SYSTRAN/faster-whisper whisper.cpp https://github.com/ggerganov/whisper.cpp https://github.com/ggerganov/whisper.cpp/tree/master/models https://huggingface.co/ggerganov/whisper.cpp/tree/main https://huggingface.co/learn/audio-course/chapter7/voice-assistant#speech-transcription | network, CNN;robot arm(motoman python control), python, whisper prompt https://tinyml.substack.com/p/your-go-to-guide-to-master-prompt https://blog.hubspot.com/marketing/write-ai-prompts https://realpython.com/practical-prompt-engineering/#improve-your-output-with-the-power-of-conversation https://www.pinecone.io/learn/series/langchain/langchain-prompt-templates/ https://www.pinecone.io/learn/series/langchain/langchain-retrieval-augmentation/ https://www.youtube.com/watch?v=efIzlP4JT6g https://github.blog/2023-07-17-prompt-engineering-guide-generative-ai-llms/ https://axk51013.medium.com/%E5%B0%88%E6%AC%84-%E5%A6%82%E4%BD%95%E7%94%A8chatgpt%E6%89%93%E9%80%A0%E4%B8%80%E5%80%8Bai%E7%94%A2%E5%93%81-part2-%E5%9F%BA%E7%A4%8Eprompt-engineering%E5%85%A5%E9%96%80-11d6cc3161ac pyttsx3 sudo apt install libttspico-utils
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# Run with default arguments and small model
./command -m ./models/ggml-small.en.bin -t 8
# On Raspberry Pi, use tiny or base models + "-ac 768" for better performance
./command -m ./models/ggml-tiny.en.bin -ac 768 -t 3 -c 0 whisper on rpi4(python≥3.8) | 生成式 AI 將影響的前 5 大領域為產品設計、軟體開發、客戶互動、行銷公關與供應鏈管理。 ffmpeg on rpi4 sudo apt update && sudo apt upgrade -y sudo apt install ffmpeg -y ffmpeg -i .format .format ex=ffmpeg -i test.mp4 out.mkv ffmpeg -i -vn ex=ffmpeg -i test.mp4 -vn out.mp3 mp3 play with rpi4 pip install python-vlc ===code below=== import vlc from time import sleep p = vlc.MediaPlayer('./five_hundred_miles.mp3') p.play() sleep(20) p.pause() sleep(2) p.play() sleep(20) p.stop() pyttsx3 on rpi4 pip install pyttsx3 sudo apt-get update && apt-get upgrade -y && sudo apt-get install espeak ### speech recognition on rpi ### sudo apt install portaudio19-dev python3-pyaudio flac espeak pip install speechrecognition sounddevice pyaudio | mistral with fine-tuning https://www.analyticsvidhya.com/blog/2023/11/from-gpt-to-mistral-7b-the-exciting-leap-forward-in-ai-conversations/ knowledge graph(KG) https://towardsdatascience.com/how-to-convert-any-text-into-a-graph-of-concepts-110844f22a1a https://github.com/rahulnyk/knowledge_graph/blob/main/extract_graph.ipynb https://towardsdatascience.com/text-to-knowledge-graph-made-easy-with-graph-maker-f3f890c0dbe8 YOLO11 https://learnopencv.com/yolo11/ |
數位轉型: https://www.cathayholdings.com/holdings/brand/fintech/ctc/trends/digital_transformation https://turingcerts.com/zh/digital-transformation/ https://alteia.com/resources/blog/artificial-intelligence-in-digital-transformation/ | ###OCR https://neuralnet.solutions/ultralytics-yolo-11-and-ollama-a-very-accurate-ocr ###powerpoint  Installation: Use pip install python-pptx python-pptx. Create Presentation: prs = Presentation(). Add Slides: slide = prs.slides.add_slide(prs.slide_layouts[layout_index]). Add Content: Title: slide.shapes.title.text = "Title". Body/Text: slide.placeholders[index].text = "Content". Save: prs.save('filename.pptx'). ... blank_layout = self.prs.slide_layouts[6] slide = self.prs.slides.add_slide(blank_layout) ... for shape in slide.placeholders: print('%d %s' % (shape.placeholder_format.idx, shape.name)) output: 0 Title 2 13 Picture Placeholder 1 placeholder = slide.placeholders[13] image = placeholder.insert_picture("甘特圖.png") | ###google langextract https://www.datacamp.com/tutorial/langextract ###vvv transforming unstructured text into interactive Knowledge Graph https://robert-mcdermott.medium.com/from-unstructured-text-to-interactive-knowledge-graphs-using-llms-dd02a1f71cd6 https://memgraph.com/blog/vector-search-memgraph-knowledge-graph-demo https://github.com/FareedKhan-dev/KG-Pipeline |