「training」の検索結果

Прогрессивная мышечная релаксация (ПМР) по Якобсону и аутогенная тренировка по Шульцу. Краткие и полные версии.

日本語の概要は準備中です。原文の説明を表示しています。

ヤコブソンによる筋弛緩法（PMR）およびシュルツによる自律訓練法。短縮版と完全版。

Audit and repair a kohya-style LoRA dataset before training. Use when the user asks to check, validate, inspect, fix, clean, 体检, or 修 a dataset, captions, or tags. Inspect image count, repeats and step budget, resolution and aspect buckets, duplicates, corrupt or non-RGB images, missing or empty captions, trigger consistency, and tag-frequency hygiene. Return a structural PASS, WARN, or FAIL report and apply only confirmed fix_dataset.py actions, dry-run first with displaced originals quarantined.

日本語の概要は準備中です。原文の説明を表示しています。

Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment workflows.

日本語の概要は準備中です。原文の説明を表示しています。

Ensure a usable SageMaker execution role exists before deploying or training. Use this skill whenever about to create a SageMaker endpoint, model, training job, or any resource that requires an execution role. Use it especially when the user has not provided a role ARN explicitly, when scripts are about to call `iam:CreateRole`, or when an AccessDenied error mentions an IAM action. Never blindly call `iam:CreateRole` — always check for existing roles first. This skill prevents the most common SageMaker deployment failure: trying to create IAM resources from an SSO principal that has no IAM write permissions.

日本語の概要は準備中です。原文の説明を表示しています。

Expert en communication de crise (incident response comms, media training, stakeholder management)

日本語の概要は準備中です。原文の説明を表示しています。

Expert en gestion du changement (ADKAR, Kotter, stakeholder mapping, resistance, training plans)

日本語の概要は準備中です。原文の説明を表示しています。

Guide for evaluation — inline evaluators, Inspect AI integration, and custom evaluators for measuring training progress. Use when the user asks about evaluation, metrics, benchmarks, or how to measure model quality during training.

日本語の概要は準備中です。原文の説明を表示しています。

Scaffold a new training recipe for the Tinker cookbook following repo conventions. Use when the user wants to create a new recipe, training script, or experiment.

日本語の概要は準備中です。原文の説明を表示しています。

Guide for checkpointing — saving, loading, and resuming training with CheckpointRecord. Use when the user asks about saving weights, resuming training, checkpoint management, or the checkpoint lifecycle.

日本語の概要は準備中です。原文の説明を表示しています。

Guide for using the Tinker Python SDK APIs — ServiceClient, TrainingClient, SamplingClient, RestClient, forward_backward, optim_step, sampling, and async patterns. Use when the user asks about Tinker API basics, how to call training/sampling, or how the SDK works.

日本語の概要は準備中です。原文の説明を表示しています。

Lesson-plan generation and compliance-check desk (Busabase App-in-Skill) for a dean of studies or training-program lead. Use when the user invokes $kelly-lesson or /kelly-lesson, mentions lesson plan, 备课, 教案, teaching plan compliance, curriculum template, dean of studies, teacher training, wants lesson plans drafted from curriculum materials and the school template, teacher drafts imported and checked against quality standards, a review queue for approving plans, or a library of approved plans exported as documents.

日本語の概要は準備中です。原文の説明を表示しています。

Kelly Education Intel: App-in-Skill daily industry intelligence cockpit for education, training, tutoring, and admissions services (Busabase App-in-Skill). Use when the user asks about education, training centers, exams, study abroad, visas, school policy, parent FAQ,招生,升学, or教育机构 sales scenes. Prepares news/source signals, buyer-intent interpretation, approved sales actions, and channel drafts for review before any external handoff.

日本語の概要は準備中です。原文の説明を表示しています。

Visualize training metrics, debug models with histograms, compare experiments, visualize model graphs, and profile performance with TensorBoard - Google's ML visualization toolkit

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。

Merge multiple fine-tuned models using mergekit to combine capabilities without retraining. Use when creating specialized models by blending domain-specific expertise (math + coding + chat), improving performance beyond single models, or experimenting rapidly with model variants. Covers SLERP, TIES-Merging, DARE, Task Arithmetic, linear merging, and production deployment strategies.

日本語の概要は準備中です。原文の説明を表示しています。

Post-training 4-bit quantization for LLMs with minimal accuracy loss. Use for deploying large models (70B, 405B) on consumer GPUs, when you need 4× memory reduction with <2% perplexity degradation, or for faster inference (3-4× speedup) vs FP16. Integrates with transformers and PEFT for QLoRA fine-tuning.

日本語の概要は準備中です。原文の説明を表示しています。

Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF, align model with preferences, or train from human feedback. Works with HuggingFace Transformers.

日本語の概要は準備中です。原文の説明を表示しています。

Simplest distributed training API. 4 lines to add distributed support to any PyTorch script. Unified API for DeepSpeed/FSDP/Megatron/DDP. Automatic device placement, mixed precision (FP16/BF16/FP8). Interactive config, single launch command. HuggingFace ecosystem standard.

日本語の概要は準備中です。原文の説明を表示しています。

The live fieldwork layer for psycho-logic — go look at the actual target market NOW instead of reasoning from your training-data stereotype of it. Use when a psycho-logic move needs grounding in a specific place: the user asks 'what do competitors in [region/country] actually do', 'what's the real price band / norm here', 'how do local leaders position themselves', 'research this market before we localise', 'what signals trust to buyers in X', or is about to launch/adapt in a market you haven't studied. Runs structured web research in the local language — local competitors and their signals, price bands, category conventions, review/trust sources, local terminology, taboos and regulations — and synthesises a compact perception profile the four moves consume. Inspired by Rory Sutherland's *Alchemy*. Routed to from psycho-logic; pair with cultural-context (the lens that tells you what to look for). 日本語・中文の相談（「現地の実態を調べて」「调研当地市场」）にも使う。

Use this skill when selecting a TPR-registered Training Provider for behind-the-wheel ELDT training. Covers the registry, evaluation criteria, and common pitfalls.

日本語の概要は準備中です。原文の説明を表示しています。

Use this skill to evaluate which defensive driving program is best for your fleet. Compares Smith System, CarriersEdge, Compass-native training, and other options.

日本語の概要は準備中です。原文の説明を表示しています。

Use this skill when documenting supervisor reasonable-suspicion training for Part 382 compliance. Covers what to log, what to retain, and what training providers to use.

日本語の概要は準備中です。原文の説明を表示しています。

Expert in 3D computer vision labeling tools, workflows, and AI-assisted annotation for LiDAR, point clouds, and sensor fusion. Covers SAM4D/Point-SAM, human-in-the-loop architectures, and vertical-specific training strategies. Activate on '3D labeling', 'point cloud annotation', 'LiDAR labeling', 'SAM 3D', 'SAM4D', 'sensor fusion annotation', '3D bounding box', 'semantic segmentation point cloud'. NOT for 2D image labeling (use clip-aware-embeddings), general ML training (use ml-engineer), video annotation without 3D (use computer-vision-pipeline), or VLM prompt engineering (use prompt-engineer).

日本語の概要は準備中です。原文の説明を表示しています。