「tuning」の検索結果

Expert patterns for Algolia search implementation, indexing strategies, React InstantSearch, and relevance 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。

TRL: SFT, DPO, PPO, GRPO, reward modeling for LLM RLHF.

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。

Unsloth: 2-5x faster LoRA/QLoRA fine-tuning, less VRAM.

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。

Axolotl: YAML LLM fine-tuning (LoRA, DPO, GRPO).

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-level process control covering feedback and feedforward control, PID tuning, advanced process control, control loop design, and process safety instrumentation.

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。

Adaption AI SDK for synthetic data augmentation and dataset adaptation. Use when building data pipelines with the Adaption Python SDK, uploading datasets (local files, Hugging Face, Kaggle), running augmentation/adaptation jobs, configuring brand controls (hallucination mitigation, safety categories, length), recipe specifications (reasoning traces, deduplication, preference pairs, prompt rephrase), evaluating dataset quality, downloading results, or any workflow involving `pip install adaption`, `from adaption import Adaption`, Adaptive Data, or the adaptionlabs.ai API. Also trigger when the user mentions synthetic data generation for fine-tuning, dataset augmentation pipelines, DPO preference pair generation, or grounding-based hallucination reduction on training data.

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 working with database architecture, MariaDB/MySQL tuning, indexing strategies, slow queries, or multi-connection patterns — even when the user just says 'this query is slow'.

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。

Wire up DevContainers / GitHub Codespaces — `devcontainer.json`, container images, secrets, VS Code features, port forwarding. NOT for tuning Copilot itself (use `copilot-config`).

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。

Domine SQL moderno com bancos de dados cloud-native, otimização OLTP/OLAP e técnicas avançadas de query. Especialista em tuning de performance, modelagem de dados e sistemas analíticos híbridos.

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。

Otimizador de banco de dados especializado em tuning de desempenho moderno, otimização de queries e arquiteturas escaláveis.

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。

Quantização pós-treinamento em 4-bits para LLMs com perda mínima de precisão. Use para implantar modelos grandes (70B, 405B) em GPUs de consumo, quando você precisa de redução de memória 4× com <2% de degradação de perplexidade, ou para inferência mais rápida (aceleração de 3-4×) vs FP16. Integra com transformers e PEFT para fine-tuning QLoRA.

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。

Assistente de Linguagem Grande e Visão. Habilita ajuste de instrução visual e conversas baseadas em imagem. Combina encoder de visão CLIP com modelos de linguagem Vicuna/LLaMA. Suporta chat multi-turno com imagem, resposta a perguntas visuais e seguimento de instruções. Use para chatbots visão-linguagem ou tarefas de compreensão de imagem. Melhor para análise conversacional de imagem.

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。

Modelo da OpenAI que conecta visão e linguagem. Permite classificação de imagens com zero-shot, correspondência imagem-texto e recuperação cross-modal. Treinado em 400M pares imagem-texto. Use para busca de imagens, moderação de conteúdo ou tarefas visão-linguagem sem fine-tuning. Melhor para compreensão geral de imagens.

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。

Rastreie experimentos de ML com logging automático, visualize treinamento em tempo real, otimize hiperparâmetros com sweeps e gerencie registro de modelos com W&B - plataforma colaborativa de MLOps

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。

Orquestração de treinamento distribuído em clusters. Escala PyTorch/TensorFlow/HuggingFace do laptop para milhares de nós. Ajuste de hiperparâmetros integrado com Ray Tune, tolerância a falhas, escalabilidade elástica. Use ao treinar modelos massivos em múltiplas máquinas ou executar varreduras distribuídas de hiperparâmetros.

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。

Improves Agent Skills via validate → rule-based eval cases → plugin-eval → prompt evals → bounded edits with held-out gates. Use when tuning skill quality, routing, or adopting Chrome/Microsoft T-named quality gates—not for bulk validate-only or SkillOpt automation.

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。

Distributed machine learning, data mining, and iterative HPC with Exasol. Covers end-to-end ML pipelines (DISTRIBUTE BY + SET scripts + BucketFS), per-entity federated training with partial_fit and ctx.reset(), batch inference, map-reduce ensemble training, distributed ensemble and SON algorithm for frequent itemset mining (Apriori, FP-Growth, association rules, market-basket analysis), Lua execute script orchestration for iterative algorithms (k-means, SGD, gradient descent), scikit-learn model training, parallel hyperparameter search, per-entity forecasting, anomaly detection, model lifecycle in BucketFS (pickle/joblib/ONNX versioning), GPU acceleration via CUDA SLCs (PyTorch/TensorFlow/RAPIDS), and ML-specific performance tuning (skew, OOM, multi-pass chunking).

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。

Optimize end-to-end application performance with profiling, observability, and backend/frontend tuning. Use when coordinating performance optimization across the stack.

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 patterns for Algolia search implementation, indexing strategies, React InstantSearch, and relevance tuning Use when: adding search to, algolia, instantsearch, search api, search functionality.

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。

Configures Microsoft Defender for Endpoint (MDE) advanced protection settings including attack surface reduction rules, controlled folder access, network protection, and exploit protection. Use when hardening Windows endpoints beyond default Defender settings, deploying enterprise-grade endpoint protection, or meeting compliance requirements for advanced malware defense. Activates for requests involving Windows Defender configuration, ASR rules, MDE tuning, or Microsoft endpoint security.

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。

Selbstjustierende Takt-Regelschleife für wiederkehrende Agenten-Scans. Nutzen, wenn ein geplanter Scan sein Intervall bei Aktivität schärfen und bei Stille abkühlen soll, ohne Operator-Eingriff.

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。

Open the alpha review screen to mark judge keepers as gold or miss, read per-miner precision, and print the eval state. Use when tuning a miner's accuracy. Thin launcher over the existing harvest engine and local review server. Triggers: "/alpha-review", "alpha screen", "open the review screen".

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。

Operational guide for the NV oOS Model Foundry (Proposal 067) — the cluster fine-tuning flywheel. Phase 1 shipped: the Corpus Foundry (Pro addon, addons/pro/includes/model-foundry/) — consent-gated, deduplicated, PII-scrubbed corpus exporters (trajectory_v1, preference_v1, docs_v1) with deterministic holdout splits and R12 provenance manifests. Covers the governance contract, row schemas, output layout, filter seams, the registration pattern for foundry tool slices, how to add a new exporter, and the test/validation gates. Use when extending the Corpus Foundry, debugging corpus exports, wiring a new data source, or continuing Phases 2–6 (train-worker, registry/serving, eval gate, fleet sharing).

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。

Optimize end-to-end application performance with profiling, observability, and backend/frontend tuning. Use when coordinating performance optimization across the stack.

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。