「model」の検索結果

Trains and fine-tunes vision models for object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm models — MobileNetV3, MobileViT, ResNet, ViT/DINOv3 — plus any Transformers classifier), and SAM/SAM2 segmentation using Hugging Face Transformers on Hugging Face Jobs cloud GPUs. Covers COCO-format dataset preparation, Albumentations augmentation, mAP/mAR evaluation, accuracy metrics, SAM segmentation with bbox/point prompts, DiceCE loss, hardware selection, cost estimation, Trackio monitoring, and Hub persistence. Use when users mention training object detection, image classification, SAM, SAM2, segmentation, image matting, DETR, D-FINE, RT-DETR, ViT, timm, MobileNet, ResNet, bounding box models, or fine-tuning vision models on Hugging Face Jobs.

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Train or fine-tune language and vision models using TRL (Transformer Reinforcement Learning) or Unsloth with Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, model selection/leaderboards and model persistence. Use for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.

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NVIDIA DeepStream model-import guidance for bringing vision models from Hugging Face or NVIDIA NGC into DeepStream pipelines with export, TensorRT build, and benchmark steps.

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Build and sharpen a project's domain model. Use when the user wants to pin down domain terminology or a ubiquitous language, record an architectural decision, or when another skill needs to maintain the domain model.

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Write and improve prompts for Qwen-Image-2.1, Alibaba's open-weight image generation and editing model. Use whenever the user wants to generate or edit images with Qwen-Image / Qwen-Image-2.1 / Qwen-Image-Edit, asks for help prompting any Qwen image model, needs readable text rendered inside an image (posters, signs, UI mockups, infographics), wants to combine several reference images into one, or asks about aspect ratios, styles, or camera settings for Qwen image generation — even when they are only choosing an image model for a text-heavy graphic and have not named the model yet.

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Expert guidance for designing, implementing, migrating, and debugging SwiftData persistence in Swift and SwiftUI apps. Use when working with @Model schemas, @Relationship/@Attribute rules, Query or FetchDescriptor data access, ModelContainer/ModelContext configuration, CloudKit sync, SchemaMigrationPlan/history APIs, ModelActor concurrency isolation, or Core Data to SwiftData adoption/coexistence.

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Run a 3-leg evaluation against a newly released LLM. Leg 1 is trap questions with deterministic answers, leg 2 is an agentic orchestration task with a planted failure, leg 3 is a brownfield feature build in a real repo. Use when the user wants to vibe-check, test, evaluate, or compare a new frontier model, or mentions model-bench, model vibe check, or launch-day protocol.

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Use to create, update or verify the Lean 4 executable specification under lean/ from the exploration documents (event-timeline, hotspots, ubiquitous-language, ux-review, model-review) — initial generation, reflecting a session's results, fixing lake build, adding a command or a screen, checking a proposed feature against the model. Formalization questions go to model-review.md as MQ, never to the expert directly.

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Generate 3D models, textures, images, rig characters, animate them, and prepare for 3D printing using the Meshy AI API. Handles API key detection, task creation, polling, downloading, and full 3D print pipeline with slicer integration. Use when the user asks to create 3D models, convert text/images to 3D, texture models, rig or animate characters, 3D print a model, or interact with the Meshy API.

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Busabase-backed App-in-Skill dashboard for a shared LLM gateway's cost and model governance — daily spend trend, cost breakdown by service/model, a canary-rollout status board, and a deterministic cost/error anomaly list. Use when the user invokes $kelly-llm-gateway or /kelly-llm-gateway, or wants to review LLM gateway spend, model routing, canary rollouts, rollback readiness, or cost/error anomalies for services routed through one shared gateway to multiple LLM providers/models. Human actions (promote/rollback/hold a rollout, acknowledge an anomaly) write directly onto the route's own Busabase record — this skill never changes a real routing config.

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Reduce LLM size and accelerate inference using pruning techniques like Wanda and SparseGPT. Use when compressing models without retraining, achieving 50% sparsity with minimal accuracy loss, or enabling faster inference on hardware accelerators. Covers unstructured pruning, structured pruning, N:M sparsity, magnitude pruning, and one-shot methods.

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Track ML experiments, manage model registry with versioning, deploy models to production, and reproduce experiments with MLflow - framework-agnostic ML lifecycle platform

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Cost modeling and ROI analysis for specialized LLM development. Use when deciding whether to train a custom model, estimating total cost, or calculating break-even vs frontier APIs. Covers training costs, inference costs, and time-to-ROI projections.

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Validates predictive models on omics and biomedical data with nested cross-validation, group/batch/temporal-aware splits, the full data-leakage taxonomy, probability calibration, decision-curve net benefit, optimism correction, sample-size planning, and TRIPOD+AI reporting. Use when estimating model performance honestly, choosing a CV scheme, detecting leakage, or judging whether reported discrimination means the model is actually useful. For feature selection itself see machine-learning/biomarker-discovery; for confirmatory-trial inference see clinical-biostatistics/trial-reporting.

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End-to-end genome-scale metabolic modeling from genome sequence to flux predictions. Covers automated reconstruction with CarveMe, model validation with memote, FBA/FVA analysis, and gene essentiality prediction. Use when building metabolic models or predicting metabolic phenotypes from genomic data.

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Statistical models library for Python. Use when you need specific model classes (OLS, GLM, mixed models, ARIMA) with detailed diagnostics, residuals, and inference. Best for econometrics, time series, rigorous inference with coefficient tables. For guided statistical test selection with APA reporting use statistical-analysis.

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OpenAI API (developers.openai.com) の Evals / Fine-tuning リファレンス。 Evals API, Datasets, graders（string check, text similarity, model, python, multi）, agent evals, trace grading, external models、 SFT, DPO, RFT（reinforcement fine-tuning）, vision fine-tuning, distillation, prompt optimizer, fine-tuning best practices。 セルフサービス fine-tuning は段階的廃止進行中。

Hermes Agent CLI リファレンス。 インストール、CLI コマンド (chat, model, gateway, config, skills, cron, webhook, mcp, profile)、 Configuration (Terminal Backends, Memory, TTS / STT, Compression, Display, Auxiliary Models)、 Features (Tools, Skills, MCP, Memory, Voice Mode, Personality, Context Files)、 Messaging Gateway (14+ プラットフォーム)、Security、Architecture、 Platform Support、Nous Portal 連携 (Tool Gateway)。

TanStack Table (React Table) v9 ヘッドレステーブル (@tanstack/react-table) の API リファレンス。 ark-ui / chakra-ui のスタイル付き Table とは別。 useTable, tableFeatures, createColumnHelper, flexRender, createCoreRowModel, sorting, column / global / fuzzy filtering, faceting, pagination, row selection, expanding, grouping / aggregation, column / row の ordering・pinning・sizing・visibility, virtualization。 v8 legacy (useReactTable / getCoreRowModel) と v9 移行。

Rive アニメーションランタイムリファレンス。 Rive クラス、useRive、useStateMachineInput、RiveComponent、 State Machine、Data Binding、ViewModel、ViewModelInstance、Events、 Layout / Fit / Alignment、.riv ファイル読み込み。 @rive-app/canvas、@rive-app/webgl2、@rive-app/react-canvas、@rive-app/react-webgl2。 Rive Scripting API (Luau)、Artboard、Property、Path / Paint / Renderer、 WGSL Shaders、GPU pipeline、Node / Layout / Converter スクリプト。

visionOS / iOS 空間コンピューティングリファレンス。RealityKit、ARKit、visionOS SwiftUI API。 Entity, ModelEntity, AnchorEntity, Component, RealityView, MeshResource, Material, System, EntityAction, GaussianSplatComponent, ComputeGraphComponent, PhysicsJointsComponent, ForceEffectComponent, ClothBodyComponent, NavigationComponent, NavigationMeshComponent, NavigationController, ImmersiveSpace, ImmersionStyle, Model3D, WindowGroup, VolumetricWindowStyle, ObjectTrackingProvider, RoomTrackingProvider, ARSession, ARWorldTrackingConfiguration, ARKitSession, WorldTrackingProvider, HandTrackingProvider, SceneReconstructionProvider。

Android アプリ開発 (Kotlin) の推奨アーキテクチャリファレンス。 Jetpack, androidx, UI layer, Domain layer, Data layer, state holder, UDF, SSOT, オフラインファースト, モジュール化, ViewModel, viewModelScope, SavedStateHandle, Lifecycle, repeatOnLifecycle, collectAsStateWithLifecycle, LiveData, Hilt による DI, Paging 3 によるページング処理。

Apple オンデバイス機械学習フレームワークリファレンス。 Core ML / Create ML / Vision / Natural Language / Speech。 MLModel, MLModelConfiguration, MLMultiArray, MLComputeUnits, MLImageClassifier, MLTextClassifier, MLDataTable, VNImageRequestHandler, VNRecognizeTextRequest, VNCoreMLRequest, NLTagger, NLLanguageRecognizer, NLEmbedding, SFSpeechRecognizer, SFSpeechAudioBufferRecognitionRequest, SFTranscription。

モデル世代交代の手順（model-roster.md 更新 → ADR-0001 確認 → drift 検査 → agents 更新 → 再測定 → カタログ追記）