'modal' 검색 결과

Generate embeddings, numerical representations of text or images, for meaning-based search, similarity comparisons, and document grouping. The skill guides model selection, multilingual processing, local execution, and integration with retrieval workflows.

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• Generate document embeddings for RAG
• Find sentences with similar meanings
• Group documents by topic

Helps adapt LLMs to specific tasks with LLaMA-Factory, including training through a web interface and memory-saving methods such as QLoRA. Provides documentation references for learning features, implementing workflows, and investigating code problems.

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• Exploring LLM fine-tuning through a web interface
• Checking LoRA and QLoRA training options
• Investigating LLaMA-Factory implementation problems

Build image captioning, visual question answering, and text-based image search with BLIP-2. The skill explains model selection, batch processing, and memory-saving settings, with Python examples for connecting image understanding to language models.

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• Generating captions for multiple images
• Building a question-answering system for images
• Searching an image collection with text

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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• Building question answering over internal documents
• Creating a chatbot that remembers the conversation
• Searching information across files, websites, and databases

Turn a feature into an agent skill or improve an existing one. Define purpose-specific quality criteria, review outputs with models from three providers, then add tests and check that user requests reach the intended skill.

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• Turn a reusable feature into an agent skill
• Audit an existing skill for missing parts
• Check quality after fixing or extending a skill

Audit instructions and memory files loaded on every agent turn. Identify repeated rules, contradictions, outdated content, and material better moved into skills, then receive evidence-backed recommendations ranked by estimated token savings and change risk.

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• Reduce the size of instructions loaded on every turn
• Find conflicting rules across instruction and memory files
• Identify workflow instructions to move into separate skills

Analyze single-cell data with scvi-tools, using deep learning to combine datasets, correct differences between experiments, and transfer cell labels. Model guides and reusable scripts cover RNA, protein, chromatin accessibility, spatial data, and downstream analysis.

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• Integrate single-cell RNA datasets and correct batch effects
• Analyze paired RNA and protein or RNA and ATAC measurements
• Transfer cell-type labels from reference data

Plan popups that encourage email signups, purchases, or inquiries. Define when they appear, who sees them, how often they repeat, and what they say, with mobile adjustments, overlap rules, and ideas for comparing alternatives.

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• Plan a newsletter signup popup
• Design an offer for visitors about to leave
• Coordinate multiple popups without overlap

Design and improve in-app screens that invite users to paid plans. Develop complete screen copy, prompt timing rules, upgrade flows, and experiment ideas based on the value users have already experienced.

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• Improve prompts shown at free usage limits
• Rewrite trial expiration screens
• Design prompts for paid-only features

Design Google Cloud systems where multiple AI agents process live audio, video, or text and respond in real time. Guides requirements discovery, architecture diagrams, infrastructure code, deployment instructions, and validation for technical guidance or safety monitoring workloads.

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• Design real-time technical guidance using live audio and video
• Plan hazard detection and visual inspection from video streams
• Create cloud architecture diagrams and deployment instructions

Build image captioning, visual question answering, and text-based image search with BLIP-2. The skill guides model selection, batch processing, and memory optimization, with examples for understanding images without training for each specific task.

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• Generate captions for multiple images
• Build a question-answering system for images
• Search an image collection using text

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.

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• Build a question-answering system for internal documents
• Create a chatbot that remembers conversation context
• Search information across files, web pages, and databases

Generate embeddings, numerical representations of text or images, for meaning-based search, similarity comparisons, and document grouping. The skill guides model selection, multilingual processing, local execution, and integration with retrieval workflows.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Generate document embeddings for RAG
• Find sentences with similar meanings
• Group documents by topic

Guides LLM fine-tuning with Axolotl, from YAML configuration and dataset formats to distributed training across GPUs. Covers LoRA and QLoRA, methods that train on preferences, multimodal support, and troubleshooting using documentation references and code examples.

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• Prepare YAML settings for LLM fine-tuning
• Explore LoRA, QLoRA, and preference training options
• Check dataset formats and input handling

Build or update Figma pages, dialogs, and panels from code or descriptions. The workflow reuses design system components, shared colors, spacing, and text styles, producing assembled views that remain linked to existing design resources.

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• Recreate an application page in Figma
• Update an existing screen to match code
• Build a dialog using shared components

Apply layered, neutral shadows to cards, form controls, panels, and larger containers using Tailwind classes with custom values. Three prescribed shadow strengths help match visual depth to the size and purpose of each component.

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• Adding subtle depth to form controls and compact cards
• Styling shadows for panels and popovers
• Giving hero media and modal-like containers stronger elevation

Builds frosted-glass interface components using translucent backgrounds, background blur, subtle borders, and soft shadows. Provides shared CSS settings and examples for consistent cards, navigation bars, modals, and buttons, with guidance on readability and browser support.

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• Creating frosted-glass cards
• Giving navigation bars and modals a consistent glass style
• Styling translucent buttons and hover states

Build Apple-inspired translucent interfaces using shared CSS settings and component patterns. The skill covers glass cards, toolbars, buttons, and modals, with light and dark modes, motion guidance, and accessibility requirements for readable text and reduced animation.

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• Designing translucent cards and buttons
• Styling glass toolbars and modals
• Supporting light and dark interface modes

Learn from top Kaggle solutions across text, images, time series, tabular data, and multimodal tasks that combine data types. Explore technical analyses, model settings, reusable code templates, and practical lessons to inform machine learning projects.

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• Study top solutions for a Kaggle competition
• Find proven methods for a machine learning task
• Explore reusable machine learning code templates

Connect Modal and create reusable cloud machines with the bundled standard image, on-demand daemon installation, and snapshot lifecycle.

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Generates BYO custom safety policies for NVIDIA Nemotron content-safety guardrails — Nemotron-Content-Safety-Reasoning-4B (text) and multimodal Nemotron-3-Content-Safety. Produces a Markdown policy, JSON taxonomy, and drop-in inference prompts. Maps rough words or an existing policy to V2 categories, adding custom categories or topic-following rules.

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Azure AI Content Understanding SDK for Python. Use for multimodal content extraction from documents, images, audio, and video. Triggers: "azure-ai-contentunderstanding", "ContentUnderstandingClient", "multimodal analysis", "document extraction", "video analysis", "audio transcription".

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Multi-Omics Factor Analysis (MOFA2) for unsupervised integration of multiple data modalities. Identifies shared and view-specific sources of variation. Use when integrating RNA-seq, proteomics, methylation, or other omics to discover latent factors driving biological variation across modalities.

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Process and generate multimedia content using Google Gemini API. Capabilities include analyze audio files (transcription with timestamps, summarization, speech understanding, music/sound analysis up to 9.5 hours), understand images (captioning, object detection, OCR, visual Q&A, segmentation), process videos (scene detection, Q&A, temporal analysis, YouTube URLs, up to 6 hours), extract from documents (PDF tables, forms, charts, diagrams, multi-page), generate images (text-to-image, editing, composition, refinement). Use when working with audio/video files, analyzing images or screenshots, processing PDF documents, extracting structured data from media, creating images from text prompts, or implementing multimodal AI features. Supports multiple models (Gemini 2.5/2.0) with context windows up to 2M tokens.

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