'mode' 검색 결과

Check Google Consent Mode v2 implementation and the consent states sent to Google services. Review advertising consent settings and help update GTM or gtag.js so tag behavior reflects users’ consent choices.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Auditing Consent Mode v2 implementation
• Updating advertising consent settings
• Checking consent signals sent to Google

Fit regression and time series models in Python, interpret coefficients, compare alternatives, and check model assumptions. Supports statistical diagnostics and forecasting, with confidence intervals and validation steps to assess uncertainty and predictive performance.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Fit regression models and interpret coefficients
• Compare models for binary outcomes or counts
• Forecast time series with prediction intervals

Structure Avalonia applications with ReactiveUI and Zafiro patterns for ViewModels, the objects that manage screen state and logic. Build multi-step wizards, organize navigation, report command progress, and connect screen layouts to their supporting logic.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Design ViewModels for screen state and logic
• Build multi-step setup wizards
• Organize navigation and discover UI sections

Connect CommunityToolkit.Mvvm ViewModels to Microsoft.Extensions.DependencyInjection, which supplies their required services. Get guidance on startup setup, instance lifetimes, messaging, view integration, and replacing dependencies in tests across supported .NET user interface frameworks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Set up dependency injection in a new XAML app
• Choose shared or per-page service instances
• Diagnose missing service registration errors

Quantize diffusion transformers with NVIDIA ModelOpt, using FP8 or NVFP4 lower-precision numbers, and prepare the saved weights for SGLang Diffusion. Follow a workflow for loading, checking output quality, and comparing speed under matching conditions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing ModelOpt FP8 weights for SGLang Diffusion
• Checking quality after diffusion model quantization
• Comparing BF16 and quantized generation speed

Convert an existing SGLang cookbook page to shared templates driven by model configuration. Preserve its deployment commands and interactive choices, transfer eligible benchmark measurements, and check equivalence, links, and rendered behavior.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Migrating a legacy SGLang cookbook page
• Moving deployment choices into model configuration
• Transferring reproducible benchmark measurements

Combine capabilities from multiple fine-tuned LLMs without retraining using mergekit. Choose a merging method, configure how models are blended, and evaluate the resulting model on tasks such as math, code generation, and conversation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Combining math, coding, and conversation capabilities
• Blending two models at different ratios
• Comparing variants made with different merging methods

Prepare to fine-tune open models or Gemini models on Agent Platform. Check the environment, convert and validate training data, choose supported models and settings, and estimate tuning costs for open models.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking whether an Agent Platform tuning environment is ready
• Converting and validating training datasets
• Choosing models and training settings for a task

Add support for a new language model in MNN, from downloading and analyzing it to mapping model fields, comparing intermediate results, and exporting files. Follow staged tests, with additional paths for image, audio, and unusual architectures.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding a new text model to MNN
• Exporting a model from HuggingFace or ModelScope to MNN
• Checking intermediate outputs against the original model

Guide knowledge distillation, where a smaller language model learns from a larger teacher. Covers training from teacher responses or prediction scores, adjusting training settings, and comparing outputs to support model compression and specialized training.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compressing a large language model into a smaller model
• Training a specialized model from teacher-generated examples
• Comparing distillation methods and training settings

Build a threat model that identifies valuable assets, attack entry points, potential threats, and mitigation priorities. Use source code and past vulnerabilities, an interview with the application owner, or a combination of both.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing security risks during system design
• Assessing an inherited codebase without its owner
• Refining a code-based draft with the application owner

Create, update, or reuse a repository threat model: a document describing system architecture, attacker capabilities, trust boundaries, and potential security failures. Produce a Markdown model grounded in source evidence for standalone use or a security scan.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating a threat model for a repository
• Updating a model after architecture changes
• Preparing a model for a security scan

Clear the active model pin in Weave Router using a short alias for unforce-model. A bundled script sends the release signal through its output, allowing the router to remove the pin and continue the current turn.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Clear the active model pin
• Request unforce-model through a short alias
• Release a model pin during the current turn

Audit Kiln’s model list against provider APIs and catalogs to identify removed models and upcoming retirements. Get findings organized by provider, then mark affected entries as deprecated after reviewing and approving the proposed changes.

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• Audit Kiln’s model list for retired entries
• Identify models approaching their retirement dates
• Check model availability for a single provider

[omh] Onboarding a newly released model generation: when a model family ships a new generation or changes its serving contract, walk the recognition, research, calibration, routing, and measurement process that keeps model handling honest and current. Use when the user says: model-optimization, model optimization, optimize for model, onboard new model, calibrate new model, new model calibration, model calibration.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

[omh] Model and provider configuration changes: diagnose role-slot model configuration, guide provider connection, and apply changes only after diff approval. Use when the user says: model-setup, hermes model setup, set up my models, set up my model, configure my models, configure model provider, connect my model provider, set up model role slots.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Document a machine learning model in a structured, transparent format for stakeholders, reviewers, and future maintainers. Use when the user says "document this model", "write a model card", "model documentation", "how should I document my ML model", "bias and fairness report", "model transparency", "what does this model do", "model handoff", "production model documentation", or needs to communicate what a model does, how it was built, where it works, and where it fails.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when the user asks about finding the best, top, or recommended model for a task, wants to know what AI model to use, or wants to compare models by benchmark scores. Triggers on: "best model for X", "what model should I use for", "top models for [task]", "which model runs on my laptop/machine/device", "recommend a model for", "what LLM should I use for", "compare models for", "what's state of the art for", or any question about choosing an AI model for a specific use case. Always use this skill when the user wants model recommendations or comparisons, even if they don't explicitly mention HuggingFace or benchmarks.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Cross-model benchmark for gstack skills. Runs the same prompt through Claude, GPT (via Codex CLI), and Gemini side-by-side — compares latency, tokens, cost, and optionally quality via LLM judge. Answers "which model is actually best for this skill?" with data instead of vibes. Separate from /benchmark, which measures web page performance. Use when: "benchmark models", "compare models", "which model is best for X", "cross-model comparison", "model shootout". (gstack) Voice triggers (speech-to-text aliases): "compare models", "model shootout", "which model is best".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Apple의 FoundationModels로 iOS 26 이상 앱에 기기 내 언어 모델을 통합합니다. 글 생성·요약, 데이터 추출, 앱 기능 호출과 생성 중 화면 갱신을 다룹니다.

• 오프라인으로 글을 생성하고 요약합니다
• 입력 문장에서 필요한 항목을 추출합니다
• AI가 앱의 자체 기능을 호출하게 합니다

Add a model page to the SGLang Cookbook using shared templates. Organize verified launch settings and measured benchmarks, create configuration files and documentation, and update navigation, vendor cards, and page validation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Add documentation for a newly supported model
• Organize verified launch recipes by hardware
• Publish measured speed and accuracy results

Use MLflow to record machine learning experiments, compare results, and manage model versions. The skill covers logging training settings, metrics, and files, organizing a model registry, and preparing models for local or cloud deployment.

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• Comparing training settings and model performance
• Recording experiments for later reproduction
• Managing model versions and release stages

Run context-mode diagnostics to check runtimes, hooks, full-text search support, plugin registration, and npm and marketplace versions. Results appear directly in the conversation with clear status labels, with a CLI fallback if the MCP call fails.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking the environment after installation
• Checking hooks and full-text search support
• Reviewing plugin registration status

Chat with NanoClaw directly inside Emacs and send selected text or org-mode sections for help. Replies can appear under child headings in your notes, supporting code review, meeting summaries, research questions, and draft revisions through a local connection.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Ask about code or errors without leaving Emacs
• Send selected code for review
• Summarize meeting notes in org-mode