“tune”的搜索结果

copy_tuner（CopyTuner / copy_tuner_client）の全 i18n キーを config/locales へ prefix 単位で移行し終えた後、 gem・初期化子・CI・deploy・ドキュメント・MCP 設定を一括撤去して copy_tuner 依存を完全に取り除くスキル。 prefix 単位の移行は copy-tuner-to-locales-migrate-prefix スキルで先に完了させておくこと。

中文简介正在准备中。当前显示原文说明。

通过命令查找和控制Bluesound及NAD播放器，查看状态、播放、暂停、停止或调整音量，还可管理播放器分组，并搜索和播放TuneIn内容。

• 查找播放器并查看状态
• 控制播放并调整音量
• 向分组添加或移除播放器

copy_tuner（CopyTuner / copy_tuner_client）で集中管理している i18n データを、prefix（正規表現）単位で Rails 標準の config/locales（YAML）管理へ移すスキル。gem の local_first_key_regexp を使うので、 gem を残したまま特定 prefix だけをローカル管理にできる（部分ローカル化）。1 回の実行で 1 prefix。 全 prefix を移して gem ごと撤去したい場合は繰り返し、完了後 copy-tuner-to-locales-cleanup スキルへ進む。 対象 prefix はスキル引数で指定でき、未指定なら export を俯瞰して選定する。

中文简介正在准备中。当前显示原文说明。

Helps adapt machine learning training to multiple GPUs and machines with Ray Train. Covers resource allocation, recovery from saved training states, and Ray Tune searches that compare settings such as learning rate and batch size.

中文简介正在准备中。当前显示英语简介。

• Adapt existing PyTorch training to multiple GPUs
• Run HuggingFace training across multiple machines
• Compare learning rates and batch sizes with Ray Tune

Provides authoritative guidance on Amazon Neptune Database and Neptune Analytics for graph, knowledge-graph, and relationship-heavy workloads — fraud detection / fraud rings, agentic memory / chatbot context across sessions, recommendations, identity resolution, Gremlin / openCypher / SPARQL queries, supernode / slow traversal, Neo4j to Neptune migration / APOC compatibility, Neptune Database vs Analytics engine selection, PageRank / community detection, GraphRAG, and connectivity from Lambda / EC2 / applications. Creates and modifies Neptune Database clusters/instances and Neptune Analytics graphs on explicit user confirmation; blocks destructive operations (delete, reset-graph, failover, major upgrade) and redirects to change-control.

中文简介正在准备中。当前显示原文说明。

Adapt machine learning training to run across multiple GPUs or machines with Ray Train. Covers changes to existing code, comparing training settings with Ray Tune, and saving checkpoints so training can resume after failures.

中文简介正在准备中。当前显示英语简介。

• Adapting existing PyTorch code for multiple GPUs
• Training HuggingFace models across multiple machines
• Comparing learning rates and batch sizes with Ray Tune

[omh] Fine-tuning a model on your own data -- SFT, DPO, RLVR or a LoRA adapter: decide first whether prompting or retrieval already closes the gap, choose the method from the data you have, and promote a checkpoint only when it beats the untuned baseline on a held-out eval. Use when the user says: model-finetuning, model finetuning, model fine-tuning, fine-tune a model, fine-tune the model, fine tune a model, fine tune the model, fine-tune.

中文简介正在准备中。当前显示原文说明。

Use this skill when the user is tuning a live or captured `doca-flow` pipeline with `doca_flow_tune` — snapshotting pipe / counter / KPI state, picking a tuning axis (rule placement, resource hints / table sizing, HW-offload mode) and a matching measurement (rule-install rate, lookup latency, hardware-counter delta), running offline or online (read-only or state-changing) modes, reading the dumper CSV / analyze JSON / visualize mermaid, or applying a recommendation back into the Flow program. Trigger even when the user does not explicitly mention "doca_flow_tune" — typical implicit phrasings include "Flow rule-install rate is low on BlueField", "table sizing looks wrong for this pipe", "tune visualize step is empty", "before/after counters don't move", or "which doca-flow knob does this recommendation hit". Refuse and route elsewhere for measuring baseline numbers (doca-flow-perf, doca-flow-dpa-perf), writing the doca-flow application, DOCA install, or streaming Flow telemetry — those belong to other skills.

中文简介正在准备中。当前显示原文说明。

Fine-tune models on Microsoft Foundry using SFT (supervised), DPO (preference), or RFT (reinforcement with graders). Covers dataset preparation, training job submission, deployment, and evaluation. USE FOR: fine-tune, SFT, DPO, RFT, training data, grader, distillation, fine-tuned model, training job, large file upload, calibrate grader, deploy fine-tuned model, evaluate fine-tuned model. DO NOT USE FOR: general model deployment without fine-tuning (use deploy-model), agent creation (use agents), prompt optimization without training (use prompt-optimizer).

中文简介正在准备中。当前显示原文说明。

Worked qupai-system (曲牌体) opera scripts, act by act. Guan Hanqing's three surviving zaju (窦娥冤／救风尘／金线池 — twelve acts, 126 tunes, one mode and one rhyme per act, sung/spoken ratios); Tang Xianzu's 牡丹亭 (55 scenes, ~420 tunes, 集唐 closing quatrains, the 圆驾 south-north composite suite); Kong Shangren's 桃花扇 (44 scenes plus four unnumbered, the 副末 frame line, upper/lower halves, a fan's seven transformations, his sixteen 凡例); 长生殿 (Wang Jilie's fifty-scene staging table); Wei Minglun's 巴山秀才 (1983 Sichuan 高腔, 43 tunes, nine 帮腔 passages). Every case states its edition and collation status, then structure, tunes, stage conventions and transferable devices. Method is in sw-chinese-opera-qupai; this skill holds the worked scripts. Use when you want a real example of how a 传奇 or 杂剧 is assembled, need actual scene counts and tune counts to imitate, are judging where a famous script breaks prosody, or need models for 副末开场, 集唐下场诗, 南北合套 or 帮腔 (曲牌体全本／牡丹亭／桃花扇／窦娥冤／救风尘／长生殿／巴山秀才／唱段表／结构表).

中文简介正在准备中。当前显示原文说明。

Fine-tune models on Microsoft Foundry using SFT (supervised), DPO (preference), or RFT (reinforcement with graders). Covers dataset preparation, training job submission, deployment, and evaluation. USE FOR: fine-tune, SFT, DPO, RFT, training data, grader, distillation, fine-tuned model, training job, large file upload, calibrate grader, deploy fine-tuned model, evaluate fine-tuned model. DO NOT USE FOR: general model deployment without fine-tuning (use deploy-model), agent creation (use agents), prompt optimization without training (use prompt-optimizer).

中文简介正在准备中。当前显示原文说明。

Evaluate whether a fine-tuned model is ready for release by checking training data, general capability loss, comparisons with the base model, and staged production rollout. Produces an evidence-based promotion or rejection report with one prioritized remedy when rejected.

中文简介正在准备中。当前显示英语简介。

• Deciding whether to release a fine-tuned model
• Checking whether training reduced general capabilities
• Reassessing a model against updated reference evaluations

Configure advanced Microsoft Defender protections on Windows, including rules that block risky behavior, protected folders, network filtering, and exploit defenses. Use audit modes to assess application impact, then deploy policies and monitor detections across an organization.

中文简介正在准备中。当前显示英语简介。

• Strengthening Windows endpoint protection
• Assessing ASR rules in audit mode
• Configuring ransomware protection for folders

copy_tuner_client v2.0.0 への移行で、削除された独自ヘルパー tt の呼び出しを Rails 標準の t（translate）へ 置換するスキル。tt は PR #122 で存在理由を失い gem から削除されたため、残った tt(...) は NoMethodError に なる。大半は機械的に tt( → t( で済むが、訳文を文字列加工している箇所（→ I18n.t へ）と label 系ヘルパーの 第一引数に渡している箇所（→ 引数構造を分離）だけは別扱いで、開発環境のマーカートークン残留バグを避けるため 1 件ずつ確認する。対象は app 配下の .rb / .haml / .erb。

中文简介正在准备中。当前显示原文说明。

このプロジェクトの i18n キー（翻訳）の参照・追加・更新・削除を行うスキル。i18n / 翻訳 / ロケール / `t` / `tt` / 翻訳キーに関わる操作のとき、および `config/locales` ディレクトリや `.yml` 翻訳ファイルを読む・参照する・編集するときは必ず使用すること。翻訳は原則 copy_tuner サーバで管理されており（一部キーのみ config/locales 管理）、素朴に config/locales を読むだけでは大半の翻訳が見つからず誤った判断をするため、必ずこのスキルの手順に従う。

中文简介正在准备中。当前显示原文说明。

八字四柱命理分析工具，属于 fortune-telling-skills 运势测算套件。基于子平真诠体系， 通过出生年月日时推算四柱八字、十神关系、五行旺衰、大运流年，揭示命局格局和人生走向。 排盘为确定性计算 + LLM 综合解读，无外部 API 依赖。 触发词：八字、四柱、命理、生辰八字、bazi、八字排盘、五行、命局分析。 不适用于：星座运势、塔罗占卜、紫微斗数 → 建议使用 fortune-hub。

中文简介正在准备中。当前显示原文说明。

Configure supervised fine-tuning for models that process images and text. Define which components remain unchanged, set LoRA adapters for updating selected model weights, choose image resolution limits, and investigate training runs whose evaluation results fail to improve.

中文简介正在准备中。当前显示英语简介。

• Configure training for an image-and-text task
• Decide whether to train vision layers
• Investigate training with flat evaluation scores

Deploy CrowdStrike Falcon sensors across Windows, macOS, and Linux endpoints. Configure threat prevention and response policies, verify installation and detection, troubleshoot sensor issues, and connect events to SIEM platforms, which collect security logs for analysis.

中文简介正在准备中。当前显示英语简介。

• Deploying Falcon sensors across enterprise endpoints
• Configuring prevention and response policies by endpoint group
• Checking sensor connectivity and test detections

Retune a collection of skills for a new model using archived runs and controlled comparisons. Establish normal measurement variation, register success criteria before editing, and make targeted changes until measured behavior meets the criteria or a specific limitation is reported.

中文简介正在准备中。当前显示英语简介。

• Investigating skill regressions after switching models
• Measuring the effect of instruction changes
• Separating behavior improvements from normal run variation

Helps choose and tune Cloud Storage FUSE mounts, which expose bucket objects through file paths. It covers cache sizing, training performance, checkpoint write safety, and cost diagnosis on GKE, Compute Engine, and Cloud Run.

中文简介正在准备中。当前显示英语简介。

• Access training data through file paths
• Choose between FUSE, native reads, and shared file systems
• Check checkpoint and concurrent write safety

Explore computer architecture and circuit design settings by repeatedly running programs, measuring results, and adjusting parameters. Compare configurations against a baseline, respect design constraints, and receive a report with the best configuration found and the search history.

中文简介正在准备中。当前显示英语简介。

• Tune cache sizes and processor settings
• Reduce circuit area while meeting timing constraints
• Compare compiler flags for runtime or code size

Fine-tune vision models on custom images using Hugging Face Jobs cloud GPUs. Covers object detection, image classification, and segmentation—identifying image regions—with dataset validation, hardware selection, cost estimates, monitoring, and saving trained models to the Hub.

中文简介正在准备中。当前显示英语简介。

• Train an object detector on custom images
• Fine-tune an image classifier
• Adapt SAM/SAM2 to custom image masks

Train or fine-tune language and vision models on Hugging Face Jobs without a local GPU. Covers dataset checks, hardware and cost planning, training monitoring, Hub storage, and GGUF conversion for local use.

中文简介正在准备中。当前显示英语简介。

• Fine-tune a language model without a local GPU
• Validate preference data before DPO training
• Estimate training costs and choose GPU hardware

Check Kiln's fine-tunable model list for deprecated or unsupported base models. Use when the user wants to audit fine-tuning support, check if fine-tune base models are still valid, or mentions fine-tune model deprecation.

中文简介正在准备中。当前显示原文说明。