「loss」の検索結果

Runs a single ScenarioRunner Python scenario (or a whole group) against a running CARLA server — FollowLeadingVehicle, ControlLoss, DynamicObjectCrossing, SignalizedJunctionLeftTurn and the rest of srunner/examples — in sync or async mode, with result output, recording and manual/agent control of the ego. Lists what is runnable on the checked-out branch and on the loaded map. Use when the user asks to "run a scenario", "test FollowLeadingVehicle", "list scenarios", "run a CARLA traffic scenario", or reports a scenario that is "not supported".

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。

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.

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。

Quantizes LLMs to 8-bit or 4-bit for 50-75% memory reduction with minimal accuracy loss. Use when GPU memory is limited, need to fit larger models, or want faster inference. Supports INT8, NF4, FP4 formats, QLoRA training, and 8-bit optimizers. 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。

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。

Battle-tested PyTorch training recipes for all domains — LLMs, vision, diffusion, medical imaging, protein/drug discovery, spatial omics, genomics. Covers training loops, optimizer selection (AdamW, Muon), LR scheduling, mixed precision, debugging, and systematic experimentation. Use when training or fine-tuning neural networks, debugging loss spikes or OOM, choosing architectures, or optimizing GPU throughput.

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。

Activation-aware weight quantization for 4-bit LLM compression with 3x speedup and minimal accuracy loss. Use when deploying large models (7B-70B) on limited GPU memory, when you need faster inference than GPTQ with better accuracy preservation, or for instruction-tuned and multimodal models. MLSys 2024 Best Paper Award winner.

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。

Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train <1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library integrated with transformers ecosystem.

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。

Train or fine-tune sentence-transformers models across `SentenceTransformer` (bi-encoder, dense or static embedding model for retrieval, similarity, clustering, classification, paraphrase mining, dedup, multimodal), `CrossEncoder` (reranker, pair scoring for two-stage retrieval / pair classification), `SparseEncoder` (SPLADE, sparse embedding model for learned-sparse retrieval), and `MultiVectorEncoder` (ColBERT / late-interaction, per-token embeddings scored with MaxSim). Covers loss selection, hard-negative mining, evaluators, distillation, LoRA, Matryoshka, and Hugging Face Hub publishing. Use for any sentence-transformers training task.

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。

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.

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。

Audit server-side paid-media measurement including server-side tag management, platform conversion APIs, event taxonomy, browser/server deduplication, consent, hashing, data quality, observability, and privacy. Use for server-side tracking, sGTM, server-side tagging, CAPI, Events API, event_id, pixel debugging, first-party measurement, or conversion data loss.

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 on how to add and propagate new metadata fields in Apache Beam's WindowedValue, extending protos, windmill persistence, and runner interfaces to avoid metadata loss.

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。

Scans Cosmos SDK blockchain modules and CosmWasm contracts for consensus-critical vulnerabilities — chain halts, fund loss, state divergence. 25 core + 16 IBC + 10 EVM + 3 CosmWasm patterns. Use when auditing custom x/ modules, reviewing IBC integrations, or assessing pre-launch chain security. Updated for SDK v0.53.x.

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 when reasoning about what survives compaction/context-loss/restart/seat-refresh, designing a high-fidelity restore packet, or distinguishing native runtime resume vs fork vs artifact-backed mental-model rebuild. Covers the 4 failure modes that prevent honest restore (compacted seat drops hot potato; restore packet preserves details but loses product intent; runtime resume mistaken for handover or fork; rebuilt seat starts with stale instructions).

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 falling leaves that read as leaves, with each one tumbling on its own axis so it presents a face, thins to an edge, and opens out again, and with its sideways slip driven by that same tumble. Covers the 2-D canvas build and the instanced-3-D variant, where leaves are recycled from, density-versus-count maths, depth layering, colour under a tone-mapped composite, reduced motion, and visibility pausing. Use for autumn maple, sakura petals, blossom, ash, snowfall shapes, or any drifting foliage where a generic particle field reads as confetti.

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。

Draws original cosy animal scenes as hand-authored SVG cards (480x360) in a grainy gouache, risograph-like style. Identifying traits are flat shapes with no outlines on a hot-pink card; soft 3-4 stop gradient shading on every rounded form, darker toward edges and base; a fine dark multiply grain that gathers at each shape's edges; a chalky purple-pink shadow pooled under the group; and big glossy eyes with cream star highlights, framed by grained autumn leaves and sparse cream sparkles. It covers design and coding moments with one cute animal at a laptop, monitor, desk, window, mug or book stack. Use when someone asks for grainy, gouache, riso, risograph, textured, cosy, cozy, autumn, hygge, storybook or children's-book style animal art, or for an empty state, onboarding step, 404 page, blog header, feature spot or marketing card with a warm handmade feel.

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 or repair game inventory, loot, equipment, tooltips, drag-and-drop, persistence, and progression systems. Use for item schemas, pickup flows, stack rules, equipment slots, atomic swaps, save migration, and no-loss regression testing.

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。

OKX Agentic Wallet — the single skill for the user's wallet and on-chain execution. Use it whenever the user wants to operate their wallet or execute an on-chain action, including: login & accounts, balance / holdings, wallet address / deposit / receive, send / transfer, contract calls (approve / deposit / withdraw), transaction history & status, message signing, wallet export & policy; pay gas with a stablecoin (Gas Station, Solana); swap / trade / buy / sell / convert, get a quote; cross-chain bridge & track arrival; limit orders (buy dip / take profit / stop loss / buy above) plus cancel / list / resume them; broadcast / gas / simulate / track a transaction; look up any public address's holdings; security scanning (token / honeypot 蜜罐 / 貔貅, DApp phishing, tx & signature checks, approvals); audit log. Once matched, follow this skill's Intent Routing to dispatch to the exact action.

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。

Operate an explicitly activated LoopX Material Lifecycle for a connected project. Use for material-store inventory, lossless migration, candidate/archive transitions, exact-read-backed ranking, ranked-entry rebuilds, bounded Explore intake, owner-gated apply, rollback, and audit. Do not use for ordinary one-off reading or research when the project has not activated Material 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。

Reprice swap transaction legs under ASC 845 (Nonmonetary Transactions) to ensure clearing accounts net to zero. Use this skill whenever the user wants to: reprice swaps, fix clearing account residuals, apply equal-value exchange to swap legs, run setBatchUseCounterpartyFiatValue, zero out a clearing account, apply ASC 845, fix swap pricing mismatches, ensure no gain/loss on swaps, or close a month where a swaps/trade clearing account has a residual balance. Also trigger when the user mentions "counterparty fiat value", "swap repricing", "clearing account net to zero", or "equal value exchange".

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。

Operate an explicitly activated LoopX Material Lifecycle for a connected project. Use for material-store inventory, lossless migration, candidate/archive transitions, exact-read-backed ranking, ranked-entry rebuilds, bounded Explore intake, owner-gated apply, rollback, and audit. Do not use for ordinary one-off reading or research when the project has not activated Material 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。

Model and publish semantic definitions in Auto Ontology. Use for terms, relationships, measures, imports, and governed results—not deployment or querying.

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。

Check progress for a detached KERMT run (pretrain, finetune, or any kermt_run_detached invocation). Reads run.json, queries docker for container state, tails the pretrain/finetune log, and parses progress lines (epoch, step, val loss).

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。

[omh] Translated product or content release: make a product or content release locale-ready with terminology, cultural-fit, and quality-review guidance. Use when the user says: localization review, translation QA, locale glossary, 현지화 검토, 번역 QA, 용어집.

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。

[omh] Unfamiliar repository needing a guided tour: create a repo map, reading path, glossary, risk map, and first-task runway for unfamiliar codebases. Use when the user says: codebase-onboarding, codebase onboarding, repo onboarding, repository onboarding, codebase tour, code tour, new repo orientation, understand this repo.

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。