'optimization' 검색 결과

Choose a model training method and configuration from paired response preferences or individual positive and negative ratings. Covers DPO, ORPO, KTO, and SimPO, including pair construction, repeated training rounds, and troubleshooting learning settings.

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• Choose a training method for paired response preferences
• Select a method for individual positive and negative ratings
• Review learning settings when DPO training loses general capability

Investigate slow SQL using execution plans, which show how a database processes a query. Review indexes, filters, joins, and maintenance practices through PostgreSQL examples to identify bottlenecks and plan changes to queries or database structure.

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• Investigating slow SQL queries
• Reviewing index choices and column order
• Reading EXPLAIN execution plans

Improve slow Apache Spark jobs by reviewing data partitions, caching, memory settings, and shuffles, which move data between workers. Use the guidance to investigate bottlenecks and tune pipelines for larger datasets.

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• Investigating slow Spark jobs
• Tuning memory and executor settings
• Reducing data skew and shuffle traffic

Review DAX formulas, the expressions used for calculations in Power BI, and propose clearer, more maintainable alternatives. Identify repeated calculations, costly filtering, and error-handling issues, then explain changes and recommend ways to test them.

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• Reviewing slow calculations in Power BI reports
• Making complex DAX formulas easier to read
• Improving division and blank-value handling

Analyze AWS infrastructure definitions, deployed resources, usage metrics, and actual costs to identify savings opportunities. Rank recommendations by savings, risk, and effort, then create individual GitHub issues and a coordinating issue after approval.

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• Identify underused AWS resources
• Estimate monthly savings from configuration changes
• Prioritize cost reductions by risk and implementation effort

Improve LLM prompts using Arize production traces, evaluation results, and human feedback. Extract current instructions, identify recurring failures, revise the prompt, and compare experiments to measure changes in output quality and catch regressions.

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• Improve a prompt using failed production traces
• Fix responses that use the wrong output format
• Review unsupported answers in retrieval-based responses

Measure denoising latency in SGLang diffusion models and identify slow operations with profiler traces. Compare performance and output quality on the same GPUs, check existing optimizations, and gather evidence before starting low-level kernel work.

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• Measuring diffusion denoising latency
• Ranking slow operations from profiler traces
• Checking whether existing optimizations address a bottleneck

Improve PostgreSQL performance through a staged workflow covering slow query analysis, indexes that speed up data access, query rewrites, configuration tuning, routine maintenance, and monitoring for production databases.

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• Investigating slow PostgreSQL queries
• Reviewing database indexes
• Tuning database configuration

Develop Azure Functions with guidance for .NET, Python, and Node.js. Covers asynchronous processing, Durable Functions for coordinating tasks, startup delay optimization, and production concerns such as timeouts, HTTP client reuse, and monitoring configuration.

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• Choose an execution model for a new .NET function
• Coordinate asynchronous tasks with Durable Functions
• Review startup delays and timeout settings

Create blog posts and social media content with a consistent brand voice, meaning a recognizable writing style. Includes tools for analyzing existing text and suggesting search optimization improvements, plus templates for adapting content and planning publication calendars.

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• Define a consistent brand writing style
• Improve blog posts for search
• Adapt articles into social media posts

Plan and improve popups and banners for email signups, purchases, and announcements. The skill helps define display timing, audiences, copy, and frequency, with mobile design guidance and comparison tests to evaluate changes.

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• Improve newsletter signup popups
• Plan offers for visitors about to leave
• Review popup timing and frequency on mobile

Reduce the memory needed to run large language models with GPTQ quantization, which stores model weights at lower precision. Covers loading compressed models, quantizing your own models, choosing execution backends, and connecting to memory-efficient fine-tuning.

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• Run a language model within limited GPU memory
• Load a pre-quantized model for text generation
• Quantize and save your own trained model

Train language models with SimPO using pairs of preferred and rejected answers. Configure preference training without a separate reference model, adapt settings for base or instruction models, and address unstable training, memory limits, or lost capabilities.

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• Training a base model with preferred and rejected answers
• Further tuning an instruction model with preference data
• Adjusting training settings for math and code tasks

Improve mobile app listings on Apple App Store and Google Play Store through keyword research, clearer descriptions, visual asset guidance, and review analysis. Create competitor reports, A/B test plans, and launch checklists to guide store page improvements.

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• Revising app titles, descriptions, and keywords
• Comparing competing apps' store listings
• Finding complaints and feature requests in reviews

Reduce GPU memory use by loading language models in 8-bit or 4-bit formats with bitsandbytes and HuggingFace Transformers. Includes configuration examples, QLoRA fine-tuning of compressed models, and optimizers that reduce memory needed during training.

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• Load larger models within limited GPU memory
• Compare 8-bit and 4-bit model settings
• Fine-tune a model with QLoRA

Compress large language models to 4-bit weights to reduce GPU memory use and accelerate inference. Covers loading compressed models, configuring quantization, and using Transformers or vLLM, with guidance on hardware requirements and backend choices.

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• Running large models with limited GPU memory
• Compressing a model with domain-specific sample text
• Serving AWQ models with vLLM

Get guidance on adapting LLMs with Unsloth, including implementation, API usage, and debugging. Covers LoRA and QLoRA, methods for adjusting models with fewer trainable parameters, with a focus on training speed and memory use.

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• Learning how to fine-tune models with Unsloth
• Exploring LoRA and QLoRA for efficient training
• Implementing features using Unsloth APIs

Configure machine learning training and batch jobs across multiple clouds with SkyPilot. The skill guides GPU selection, lower-cost cloud and region choices, and automatic recovery when discounted spot instances are interrupted.

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• Choose a lower-cost cloud for GPU training
• Run long training jobs with recovery after interruptions
• Configure distributed training across multiple machines

Write, review, and refactor React and Next.js code using performance guidelines ranked by impact. Covers sequential data requests, downloaded code size, server work, and unnecessary rendering to guide concrete implementation improvements.

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• Review React and Next.js code for performance issues
• Improve data fetching that waits on sequential requests
• Reduce downloaded code size and loading time

Review code for performance bottlenecks across database queries, memory use, frontend rendering, network requests, and algorithms. Use a structured checklist to identify optimization opportunities and classify their severity according to expected load and scale.

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• Reviewing database queries for avoidable overhead
• Checking memory leaks and unbounded resource growth
• Reviewing React rendering and long-list performance

Write, review, or refactor React code using prioritized guidance for data fetching, bundle size, rendering, component structure, testing, and type safety. Start with high-impact issues such as sequential requests and unnecessary library loading.

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• Writing React components and data-fetching logic
• Reviewing code for loading and rendering performance
• Refactoring large components and overly broad interfaces

Plan popups that encourage email signups, purchases, or inquiries. Define when they appear, who sees them, how often they repeat, and what they say, with mobile adjustments, overlap rules, and ideas for comparing alternatives.

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• Plan a newsletter signup popup
• Design an offer for visitors about to leave
• Coordinate multiple popups without overlap

Produce blog posts, articles, and guides from research through a finished draft. Build an outline from audience needs and search intent, gather sources, then refine readability, brand voice, search optimization, links, and metadata for publication.

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• Researching competitors and preparing an article brief
• Writing a complete blog post or guide
• Improving search optimization and readability

Audit and improve content intended for citation in AI answers. Review evidence, author information, and structure, generate revised content and structured metadata, and keep a local record of observed citations across AI services.

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• Audit existing articles for evidence and credibility gaps
• Revise article structure and references for AI answers
• Record citations observed across AI services