From Meeting Audio to AI-Generated Minutes with Whisper and LLaMA
Writing meeting minutes has always been a time-consuming task. Thanks to advances in AI, we can now automate the process: In this post, we’ll walk through the full workflow. Step …
Efficient Multi-Model Inference with 4-bit Quantization in Hugging Face Transformers
Introduction In this large language model Colab tutorial, I’ll walk you through how to efficiently load and run multiple large language models (LLMs) in Hugging Face Transformers using 4-bit quantization …
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How to Run Hugging Face Models on Google Colab
Hugging Face provides a wide variety of pre-trained models for tasks like text generation, summarization, translation, and more. Google Colab, on the other hand, offers free cloud-based Jupyter notebooks with …
Building an Agentic Chatbot: Memory, Speech, and Image Generation with LLMs
Introduction Mention that this blog demonstrates how to build a chatbot that can respond with text, speech, and images while remembering conversation context. Briefly introduce Agentic AI: LLMs that can …
Extending GPT with External Tools: How AI Can Call APIs for Smarter Answers
Extending OpenAI with External Tools When working with OpenAI models, you might face situations where the model cannot directly provide real-time or domain-specific answers — such as looking up the …
GPT and Claude In Gradio UI
Gradio is an open-source Python framework that makes it easy to build interactive web-based UIs for machine learning (ML) models, data pipelines, or any Python function. Instead of writing frontend …
Adversarial Conversations between Chatbots (Core Concept of the Context Window)
You’re already familar with prompts being organized into lists like: In fact this structure can be used to reflect a longer conversation history: Import Rule: Please remember this prompt rule. …
From One-Shot to Multi-Shot Prompting
One-Shot The Website class retrieves the text content and links from a given URL. Prompt 1: Here’s an OpenAI example showing how to use a one-shot prompt to get a …
這是一個非常好的問題！讓我來解釋為什麼ChatGPT需要1億1700萬個參數，而線性回歸只需要20-200個參數。
🔍 問題的複雜度差異 Linear Regression (線性回歸) ChatGPT (語言模型) 🧠 參數用途大解析 1. 詞彙表示 (Vocabulary Embeddings) 光是把每個詞轉換成電腦能理解的數字就需要2560萬個參數！ 2. 多頭注意力機制 (Multi-Head Attention) 3. 多層堆疊 🤔 為什麼需要這麼多參數？ 複雜度對比 任務輸入複雜度輸出複雜度所需知識線性回歸數值特徵單一數值數學關係ChatGPT自然語言創意文本人類所有知識 語言的複雜性 1. 語義理解 2. 上下文依賴 3. …
JavaScript Essential for React
React: The library for web and native user interfaces Destructing Objects and Arrays Rest/Spread Operator Spread Operator (…) Expands (spreads) an array or object into individual elements. Rest Operator (…) …