Results for “lightning-modal”

Gemini: A Family of Highly Capable Multimodal Models

Implements sibling communication between Lightning Web Components using Lightning Message Service (LMS) or the legacy pubsub utility, covering message channel definition, publish/subscribe patterns, and scope selection.

Builds a custom LWC lookup component with typeahead search via Apex SOSL/SOQL, debounce, keyboard navigation, and pill UI for cases where standard pickers fall short.

Chameleon: Mixed-Modal Early-Fusion Foundation Models

Guides Lightning Web Component developers in setting up lightning-datatable with stable row identity, typed columns, row actions, inline edit, and bounded infinite loading.

Organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, implement data pipelines, callbacks, logging (W&B, TensorBoard, MLflow), and distributed training (DDP, FSDP, DeepSpeed) for scalable neural network training.

Entrena modelos de machine learning con Scikit-learn, LightGBM y XGBoost, desde un baseline hasta un modelo productivo con validación robusta y explicabilidad.

Theme Salesforce Lightning base components and custom LWCs using SLDS Styling Hooks, covering global and component hooks, shadow DOM considerations, Experience Cloud theming, and SLDS 2 migration.

Builds fast, standard-compliant web components with Lit, reactive properties, and Shadow DOM. Use for framework-agnostic UI.

High-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices.

Use when you need a dark atmospheric background with drifting vertical light folds, screen-blended glow, and a concentrated luminous corner or lower-edge bloom — background atmosphere only, not a full layout system.

Meta's 7-8B specialized moderation model for LLM input/output filtering. 6 safety categories - violence/hate, sexual content, weapons, substances, self-harm, criminal planning. 94-95% accuracy. Deploy with vLLM, HuggingFace, Sagemaker. Integrates with NeMo Guardrails.

Organizes PyTorch code with a Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks, and minimal boilerplate. Scales from laptop to supercomputer with the same code.

Guides secure design and review of Lightning Web Components, covering DOM safety, Apex data exposure, and Lightning Web Security.

High-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices.

Use Light DOM in LWC when third-party libraries or global CSS require document-level access, with guidance on scoped styles and managed-package constraints.

Routes to 82 Lightning Web Components skill packages covering reactivity, wire adapters, component communication, accessibility, performance, security, and Jest testing. Finds and opens the exact skill package for LWC-related requests.

Multimodal Learning with Transformers: A Survey

Builds Lightning Web Components for record-page quick actions, covering screen actions with modals and headless actions with no UI, including lifecycle, recordId injection, and post-action UX.

当需要在2-3个月内集中精力攻克一个重大、有挑战性的个人或职业目标时，使用此模型

Use when composing corner-anchored laser accents with thin beams, a bright emitter node, bloom, and atmospheric glow or fog. Prefer this over mengto-webgl-laser when lasers are layout corner ornaments, not a fixed full-screen WebGL beam.

Build applications with Gemini API hosted models, including multimodal content, function calling, and structured outputs, using the latest SDKs and model specifications.

Pixtral 12B: A Frontier Multimodal Model

Builds memory-safe, performant Lightning Web Components with correct lifecycle hook usage, covering build, review, and troubleshooting workflows.

Meta's 7-8B specialized moderation model for LLM input/output filtering. 6 safety categories - violence/hate, sexual content, weapons, substances, self-harm, criminal planning. 94-95% accuracy. Deploy with vLLM, HuggingFace, Sagemaker. Integrates with NeMo Guardrails.

Render long lists (1k+ rows) efficiently in Salesforce LWC using lightning-datatable infinite loading or an IntersectionObserver sentinel, with keyset pagination and accessibility guidance.

Emu: Generative Pretraining in Multimodality

Emu2: Generative Multimodal Models are In-Context Learners

Guides designing or reviewing Lightning Web Components for keyboard access, semantic labeling, and focus management, prioritizing base components over custom ARIA.

Crea un sistema de diseño consistente con múltiples temas (light/dark/custom), Material 3, tipografía personalizada y cambio dinámico de temas en Flutter.

Use when adding a fixed full-screen WebGL laser background with a thin white-hot vertical core, restrained brand-colored halo, and soft smoky fog. Prefer this over mengto-corner-lasers when the beam is a full-viewport WebGL background, not corner ornaments; not for full page layout, copy, or unrelated particle systems.

Dolphins: Multimodal Language Model for Driving

Documents Linear's dark-canvas marketing design system with near-black backgrounds, lavender-blue accent, four-step surface hierarchy, aggressive negative tracking on display type, and product UI screenshots as the primary visual rhythm.

Deploy Meta's LlamaGuard moderation model to filter LLM inputs and outputs across 6 safety categories using HuggingFace, vLLM, or FastAPI.

Design a theming architecture that supports brand variants, dark mode, and high-contrast modes with token mapping.