'accuracy' 검색 결과

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

Design and refine prompts for language model applications. Choose useful examples, create reusable templates and system instructions, and compare prompt variations using accuracy, consistency, response time, and token usage to guide improvements.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Refine prompts that produce inconsistent answers
• Create reusable templates with variables and conditions
• Select relevant input-output examples for a task

Fine-tune large language models with LoRA and QLoRA while limiting GPU memory use. Guides training settings and adapter management, using small sets of task-specific weights that can be saved, switched, or merged into a base model.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Fine-tuning an LLM with limited GPU memory
• Choosing LoRA settings and training layers
• Switching task-specific adapters on one base model

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

Reduce the size of AGENTS.md or RESOLVER.md by grouping skills into functional areas. Each area keeps a list of its sub-skills, helping the agent find the appropriate skill through a two-level routing structure.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Shortening a large AGENTS.md
• Grouping skill routes by functional area
• Checking routing accuracy after compression

Analyze differences between financial budgets, actual results, forecasts, or prior periods. Break changes into measurable drivers, explain business causes, and prepare management commentary with waterfall breakdowns that show how each factor contributes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Explain differences between budget and actual results
• Break down revenue or expense changes by driver
• Prepare variance commentary for management

Review an analysis before sharing it with stakeholders. Check calculations, data selection, comparison methods, potential bias, charts, and whether conclusions follow from the evidence, then receive a readiness assessment and specific improvements.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing an analysis before an executive presentation
• Spot-checking SQL results and aggregation logic
• Checking comparisons across different time periods

Route customer success, sales, revenue operations, and contract requests to one of four specialist skills. Use it when a business request is unclear, so the agent selects the relevant workflow before starting the work.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choose a skill for identifying accounts at risk of churn
• Route RFP participation and proposal requests
• Find the workflow for pipeline and revenue forecasts

Design an enterprise search system that answers questions using retrieved company content, an approach called RAG. Gather requirements and produce architecture diagrams, deployment guidance, and validation plans using GKE, AlloyDB, and open models.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing conversational search for internal documents
• Planning RAG with a vector-enabled SQL database
• Hosting open models on GKE

Design evaluation systems that measure agent results across accuracy, completeness, citations, and tool efficiency. Build repeatable tests, compare changes against a baseline, and define quality checks that catch regressions before deployment and monitor production performance.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare agent configurations against a baseline
• Catch quality regressions before deployment
• Measure the effects of context strategies

Explore how coordinating agents can pass relevant working state to workers through KV cache, a model's internal attention memory. Covers task-specific selection, runtime requirements, and comparisons of accuracy, token use, and processing overhead.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reducing repeated history sent to worker agents
• Comparing internal memory sharing with text summaries
• Assessing runtime support for KV cache compaction

Check BibTeX references against web records and export field differences in a spreadsheet. Independent checking depends on an isolated web agent; without isolation, results flag potential issues rather than certify matching entries.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking bibliography fields before paper submission
• Finding author omissions and ordering errors
• Reviewing reference discrepancies in a spreadsheet

Compare local BibTeX references with independently retrieved web records to identify missing or reordered authors, incorrect years, venues, and identifiers. Export a side-by-side spreadsheet with per-reference verdicts and evidence for discrepancies.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking bibliography fields before paper submission
• Finding author omissions and ordering errors
• Reviewing reference discrepancies in a spreadsheet

Turn a research paper into a conference poster using its figures and findings. Produce one HTML/CSS file and a print-ready PDF, with measured layout checks and Gemini reviews of content accuracy and final appearance.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create a conference poster from a research paper
• Redesign an existing research poster
• Check poster claims against the source paper

Creates figures for machine learning papers from draft text or experiment data. Extracts components for Gemini-generated diagrams, chooses suitable charts with matplotlib or seaborn, and adjusts colors, fonts, and export formats for publication.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turn a method section into an architecture diagram
• Create comparison charts from experiment results
• Plot training loss and accuracy over time

Build safety checks for LLM prompts and responses using Meta's LlamaGuard. This skill covers content classification, batch moderation, serving a moderation API, and connecting checks to NeMo Guardrails.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Screening user prompts before sending them to an LLM
• Checking generated responses before displaying them
• Building an API for content safety checks

Helps fine-tune large language models with LoRA and QLoRA, methods that train small sets of additional parameters. Covers memory-conscious training, configuration choices, saving adapters, and switching task-specific adapters on a shared base model.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Fine-tune a model with limited GPU memory
• Choose LoRA settings for a specific task
• Switch task-specific adapters on one base model

Compress large language models to 4-bit weights to reduce GPU memory needs and accelerate text generation. Covers loading compressed models, converting your own models, choosing settings, and preparing memory-efficient fine-tuning with transformers and PEFT.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Run language models with limited GPU memory
• Convert your own model to 4-bit weights
• Compare compression settings for quality and speed

Helps reduce GPU memory use by loading large language models in 4-bit or 8-bit formats, a technique called quantization. Covers bitsandbytes configuration, memory checks, QLoRA fine-tuning, and training with memory-saving optimizers.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Load a larger model within available GPU memory
• Compare 4-bit and 8-bit quantization settings
• Fine-tune a quantized model with QLoRA

Design and implement RAG systems, which retrieve documents to inform AI answers. Get document preparation strategies, vector database comparisons, combined keyword and meaning-based search, result reranking, and an evaluation plan for retrieval quality.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Design an AI application that answers using internal documents
• Compare vector databases and document splitting strategies
• Improve search through keyword matching and result reranking

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

Track machine learning experiments with Trackio by recording training measurements, viewing live dashboards, and flagging diagnostic events. Retrieve metrics and alerts through command-line queries, with JSON output for automated monitoring and experiment review.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Recording training loss and accuracy
• Flagging loss spikes or stalled training
• Monitoring cloud training through a Space dashboard

Guides a Worktrunk release from testing and change review to version updates, release notes, tagging, and publication checks. Includes reviews of potential data loss, documentation accuracy, and contributor credits before the release is published.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing a new Worktrunk release
• Writing release notes from code changes
• Reviewing data-loss risks before release

Create clear, accurate charts for dashboards, data exploration, and reports. Choose chart types that fit the message, refine labels and scales, use accessible colors, and add context and annotations to help readers understand the findings.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing a sales dashboard for executives
• Exploring patterns in customer behavior
• Creating charts for an annual report