「tuning」の検索結果

Hugging Face Transformers for loading Hub models, running pipeline inference, text generation, and Trainer fine-tuning on NLP, vision, audio, and multimodal tasks. Applies when working with AutoModel, pipelines, tokenizers, generation configs, or TrainingArguments within Transformers.

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Supports machine learning in Python with scikit-learn. Applies when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices.

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Modal is a serverless cloud platform for running Python on demand, including on-demand GPUs. Use when deploying or serving AI/ML models, running GPU-accelerated workloads (training, fine-tuning, inference), serving web endpoints, scheduling batch jobs, or scaling Python code to cloud containers with the Modal SDK.

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

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

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Prepare, format, and validate datasets for supervised fine-tuning and preference training. Use when converting raw data into training format, applying chat templates, configuring sequence packing, generating synthetic training data, or writing a dataset card before a run.

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Optimize Apache Spark jobs with partitioning, caching, shuffle optimization, and memory tuning. Use when improving Spark performance, debugging slow jobs, or scaling data processing pipelines.

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Use this skill whenever the user mentions IP geolocation feeds, RFC 8805, geofeeds, or wants help creating, tuning, validating, or publishing a self-published IP geolocation feed in CSV format. Intended user audience is a network operator, ISP, mobile carrier, cloud provider, hosting company, IXP, or satellite provider asking about IP geolocation accuracy, or geofeed authoring best practices. Helps create, refine, and improve CSV-format IP geolocation feeds with opinionated recommendations beyond RFC 8805 compliance. Do NOT use for private or internal IP address management — applies only to publicly routable IP addresses.

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Generate D2 database diagrams from Entity Framework Core models. USE FOR: EF Core database diagram, Entity Framework Core ERD, DbContext diagram, C# entity relationship diagram, PostgreSQL schema visualization, generate .d2 file from EF Core entities, Fluent API mapping diagram, migrations-based database diagram, table relationships, owned types, many-to-many join tables, indexes and constraints. DO NOT USE FOR: runtime debugging, database migration execution, schema deployment, SQL performance tuning, or draw.io diagrams.

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Optimizes, improves, and debugs LLM prompts using production trace data, evaluations, and annotations. Extracts prompts from spans, gathers performance signal, and runs a data-driven optimization loop using the ax CLI. Use when the user mentions optimize prompt, improve prompt, make AI respond better, improve output quality, prompt engineering, prompt tuning, or system prompt improvement.

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Autonomous iterative experimentation loop for any programming task. Guides the user through defining goals, measurable metrics, and scope constraints, then runs an autonomous loop of code changes, testing, measuring, and keeping/discarding results. Inspired by Karpathy's autoresearch. USE FOR: autonomous improvement, iterative optimization, experiment loop, auto research, performance tuning, automated experimentation, hill climbing, try things automatically, optimize code, run experiments, autonomous coding loop. DO NOT USE FOR: one-shot tasks, simple bug fixes, code review, or tasks without a measurable metric.

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Detects ransomware encryption activity in real time using entropy analysis, file system I/O monitoring (Sysmon, watchdog, psutil), and behavioral scoring to identify mass file modification, abnormal entropy spikes in written data, and suspicious process behavior characteristic of encryption routines. Use when building real-time ransomware detection, tuning entropy thresholds, or investigating suspected active encryption on an endpoint.

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Implement User and Entity Behavior Analytics (UEBA) using Elasticsearch/OpenSearch to build behavioral baselines, calculate anomaly scores, perform peer group analysis, and alert on insider threat indicators such as data exfiltration, privilege abuse, and unauthorized access. Use when building or tuning a UEBA pipeline rather than a one-off manual hunt.

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Installs, configures, and tunes Snort 3 to monitor network traffic for malicious activity using custom and community rulesets, preprocessors, and alert output plugins. Use when deploying network-based intrusion detection at key boundaries, writing custom Snort rules, tuning rulesets to reduce false positives, or integrating Snort alerts with a SIEM.

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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.

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

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Master modern SQL with cloud-native databases, OLTP/OLAP optimization, and advanced query techniques. Expert in performance tuning, data modeling, and hybrid analytical systems.

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PostgreSQL database optimization workflow for query tuning, indexing strategies, performance analysis, and production database management.

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Expert database optimizer specializing in modern performance tuning, query optimization, and scalable architectures.

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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.

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Expert guidance for GRPO/RL fine-tuning with TRL for reasoning and task-specific model training

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Track ML experiments with automatic logging, visualize training in real-time, optimize hyperparameters with sweeps, and manage model registry with W&B - collaborative MLOps platform

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OpenAI's model connecting vision and language. Enables zero-shot image classification, image-text matching, and cross-modal retrieval. Trained on 400M image-text pairs. Use for image search, content moderation, or vision-language tasks without fine-tuning. Best for general-purpose image understanding.

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Use when working with cognee's session memory or improve() — storing conversation turns, agent traces and feedback with session_id, bridging sessions into the permanent graph, reading an ImproveResult, understanding why an improve stage was skipped, already_completed or lock_held, or tuning the IMPROVE_* settings.

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。