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Helps build Python workflows that extract information from documents, images, audio, and video using Azure AI Content Understanding. Covers document text, timed transcripts, video frames, and invoice fields for search and automated processing.
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Design and implement LLM applications, AI agents, and RAG systems that retrieve documents to support answers. Cover model selection, search quality, testing, monitoring, safety measures, and cost controls for applications intended for production use.
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Integrate or upgrade cache-dit in SGLang diffusion, the image generation system. Follow guidance for resolving code conflicts, adding cache settings, testing feature combinations, and comparing generated images using quality measurements and logs.
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Investigate phishing emails that hide malicious links in QR codes. Decode destinations, analyze suspicious URLs, configure email detection rules, and plan mobile protections and awareness training for people who scan codes on their phones.
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Build a screening pipeline for web pages, PDFs, and images before an AI agent reads them. Extract hidden text, check for malicious instructions with detection models, and record decisions to block or allow content.
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Compress large language models to 4-bit weights to reduce GPU memory use and accelerate inference. Covers loading compressed models, configuring quantization, and using Transformers or vLLM, with guidance on hardware requirements and backend choices.
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Build image generation workflows in Python with Stable Diffusion and HuggingFace Diffusers. The guide covers text prompts, transforming existing images, replacing selected regions, controlling composition, applying learned styles, and adjusting memory use during generation.
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Get guidance on adapting large language models with Axolotl. Covers YAML configuration files, LoRA and QLoRA methods for training fewer parameters, distributed training settings, dataset formats, and debugging training code.
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Use SAM to identify object regions in images from points, boxes, or existing masks. The skill provides implementation examples for transparent cutouts, automatic mask generation, annotation tools, and model selection based on available resources.
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Build image classification and search workflows by comparing images with text descriptions. Covers classification without additional training, similarity scoring, batch processing, and storing image representations in a vector database for retrieval.
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Turn audio and video into text with Whisper, including Japanese and other supported languages. This skill covers transcription, English translation, timestamped subtitles, model selection, and batch processing, with guidance on hardware needs and recognition limitations.
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Build image-based conversations with LLaVA, an AI model that combines image understanding with language. Covers setup, model selection, image descriptions, follow-up questions, and memory-saving options for running models on a GPU.
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Generate music and sound effects from written descriptions with AudioCraft. Covers MusicGen and AudioGen, using reference melodies or musical styles, producing stereo audio, adjusting generation settings, and saving generated sounds as audio files.
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Prepare training datasets for large language models with GPU-assisted cleaning. Covers duplicate removal, quality filtering, and personal information redaction, with workflows for text, images, video, and audio using NeMo Curator.
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Build LlamaIndex applications that retrieve relevant material before generating answers, a method called RAG. Connect documents, websites, and databases to support document questions, conversational search, structured summaries, and checks of answer relevance and source consistency.
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Build n8n workflows that download, transform, and send files without losing their contents. Learn how file data differs from JSON, how to preserve attachments, and how to use storage keys or URLs with AI tools and chat messages.
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Build Gemini API integrations on Google Cloud’s Agent Platform using the Google Gen AI SDK. Get guidance on authentication, text and media processing, structured JSON responses, function calls, caching, batch processing, and real-time voice or video interactions.
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Train sentence-transformers models for search, similarity, and reranking, which reorders search results by relevance. Choose a model type, prepare data, configure training, compare results against a baseline, and publish the trained model to Hugging Face Hub.
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Build text, image, and audio features in JavaScript using machine learning models that run in browsers or server runtimes. Covers model selection, execution settings, caching, and memory cleanup without requiring a Python server.
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Guides image classification and search with OpenAI's CLIP by comparing images with text descriptions, without additional training. Includes batch processing, image-text matching, and category-based checks for potentially inappropriate images.
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Turn recorded audio and video into text or timestamped subtitles with Whisper. This skill covers transcription across 99 languages, language detection, translation into English, batch processing, and model selection for available computing resources.
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Guides building AI applications that search documents before answering questions, an approach called RAG. Covers importing files and web data, creating searchable indexes, adding conversational interfaces, and evaluating whether answers match the retrieved material.
中文简介正在准备中。当前显示英语简介。
Generate music and sound effects from written descriptions with AudioCraft. Covers MusicGen and AudioGen, using reference melodies or musical styles, producing stereo audio, adjusting generation settings, and saving generated sounds as audio files.
中文简介正在准备中。当前显示英语简介。
Build image generation workflows in Python with Stable Diffusion and HuggingFace Diffusers. The guide covers text prompts, transforming existing images, replacing selected regions, controlling composition, applying learned styles, and adjusting memory use during generation.
中文简介正在准备中。当前显示英语简介。