'model' 검색 결과

Power BI semantic modeling assistant for building optimized data models. Use when working with Power BI semantic models, creating measures, designing star schemas, configuring relationships, implementing RLS, or optimizing model performance. Triggers on queries about DAX calculations, table relationships, dimension/fact table design, naming conventions, model documentation, cardinality, cross-filter direction, calculation groups, and data model best practices. Always connects to the active model first using power-bi-modeling MCP tools to understand the data structure before providing guidance.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

대규모 조사나 생성 작업을 여러 AI 모델에 분담해 계획 검토, 병렬 실행, 결과 검증과 통합을 진행합니다. 담당별 진행 상황과 재시도도 기록합니다.

• 여러 경쟁사를 병렬로 조사
• 대규모 생성 작업 분담
• 조언자에게 계획이나 초안 검토 요청

최신 gstack 템플릿에 맞춰 iOS 앱의 디버깅 연결 코드를 다시 생성합니다. 관련 Swift 파일과 추가된 상태를 타입에 맞게 읽고 쓰는 접근자를 갱신합니다.

• gstack 업그레이드 후 디버깅 연결 갱신하기
• 추가한 ViewModels에 대응하기
• 추가 속성의 접근자를 다시 생성하기

Develop machine learning models from local data, covering data preparation, model selection, training, evaluation, and deployment preparation. Use it to build workflows for tasks such as predicting customer churn or classifying images.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building a model to predict customer churn
• Training a neural network for image classification
• Preparing missing values and categorical data for training

Use Hugging Face Transformers to load pretrained models, generate text, and classify text, images, or audio. Prepare model inputs, control generation settings, and fine-tune models on custom datasets with the Trainer API.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Prototype text, image, or audio classification
• Configure text generation and conversational responses
• Fine-tune a model on a custom dataset

Complete an existing financial model template by entering historical data and forecast assumptions, then linking the income statement, balance sheet, and cash flow statement with formulas. Check statement balances, cash consistency, and scenario behavior as each section is completed.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Complete a partially filled financial model
• Link forecasts across three financial statements
• Check balance sheet and cash flow consistency

Guide changes and reviews for SGLang’s Scheduler, TokenizerManager, and ModelRunner classes. Keep initialization in small, replaceable helpers and separate computation from coordination so large classes retain clear responsibilities and explicit dependencies.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing changes to SGLang’s three large classes
• Splitting initialization into replaceable helpers
• Moving computation out of ModelRunner

Monitor requests to an AI prediction API for signs of model copying or training-data exposure. Test an authorized model with simulated attacks, measure the risk, and evaluate defenses such as request limits and reduced prediction detail.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Monitoring a public or partner prediction API
• Measuring model copying risk before deployment
• Testing request limits and reduced prediction detail

Build and evaluate Mamba models for long sequences using state-space models, which summarize past inputs in an internal state. Covers setup, pretrained text generation, differences between Mamba-1 and Mamba-2, and speed comparisons with Transformers.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Generate text with pretrained Mamba models
• Compare Mamba-1 and Mamba-2 configurations
• Benchmark generation speed against Transformers

Assess language models with over 60 academic benchmark tests using consistent prompts and scoring. Compare models, track performance during training, and organize research results with support for HuggingFace models, vLLM, and APIs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare models on standard knowledge and reasoning tests
• Track model performance across training checkpoints
• Organize evaluation results for research reports

Evaluate code generation models with HumanEval, MBPP, and other benchmarks. Test generated programs, compare models across programming languages, and record pass@k scores, which estimate the chance that at least one of k solutions passes the tests.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing code generation models on shared benchmarks
• Measuring correctness across programming languages
• Evaluating models trained to follow instructions

Build formula-driven Excel financial models for linked financial statements, business valuation, and fundraising forecasts. Separate assumptions, calculations, and results, compare scenarios, and check that statement balances and saved calculation results are consistent.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build linked income, balance sheet, and cash flow forecasts
• Estimate business value with discounted cash flows
• Compare results under different assumptions

Check a user prompt through an existing Google Model Armor template. Supply the text directly, submit a JSON request, or pass text through standard input, which lets the command read piped content.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking an AI prompt against an existing template
• Passing prompt text from another command
• Submitting a prompt check as a JSON request

Plan and analyze A/B tests, prepare tabular data, and train and evaluate prediction models. Estimate intervention effects from observational data, track model experiments with MLflow, and translate statistical findings into business decisions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning A/B test sample sizes and analyzing results
• Preparing tabular data for prediction models
• Comparing model performance and recording experiments

Call Gemini and OpenMaaS models such as Llama, DeepSeek, and Qwen through Google Cloud Agent Platform. Prepare authentication and SDK code, run prompts, configure connection endpoints, and troubleshoot common request errors.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Send a test prompt to Gemini
• Call Llama or DeepSeek with the OpenAI SDK
• Write code to call an existing custom endpoint

Evaluate code generation models with HumanEval, MBPP, and multilingual benchmarks. The skill guides test execution and model comparisons using pass@k, an estimate of whether at least one of k generated solutions passes the tests.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare code generation models on shared benchmarks
• Measure code generation performance across languages
• Evaluate instruction-tuned coding models

Compress large language model weights to four bits with AWQ, a method that uses model activation patterns to limit quality loss. Follow workflows for quantization, loading compressed models, and serving them with vLLM using less GPU memory.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Running large language models with limited GPU memory
• Quantizing a model with domain-specific text
• Serving an AWQ model with vLLM

Implement Mamba, a model architecture that processes sequences through an internal state with computation scaling linearly with input length. Covers pretrained text generation, Mamba-1 and Mamba-2 configuration, speed comparisons with Transformers, and GPU memory troubleshooting.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Explore models for long sequences or streaming applications
• Generate text with pretrained Mamba models
• Compare Mamba-1 and Mamba-2 configurations

Assess language models with over 60 academic benchmark tests using consistent prompts and scoring. Compare models, track performance during training, and organize research results with support for HuggingFace models, vLLM, and APIs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare models on standard knowledge and reasoning tests
• Track model performance across training checkpoints
• Organize evaluation results for research reports

Build machine learning workflows that connect data checks, training, evaluation, and deployment. The skill implements experiment tracking, model version management, and automated retraining, with reproducible environments and explicit validation thresholds before models enter production.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Automate model training and retraining workflows
• Record and compare machine learning experiments
• Build a feature store for model input data

Choose a serving container for models deployed on SageMaker. Match the model type to a compatible image, retrieve its address from the AWS catalog, and check regional availability, CPU or GPU variants, and required host settings.

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• Choose a container for an LLM deployment
• Select images for embeddings and rerankers
• Check regional image addresses and GPU compatibility

Create, update, or reuse a repository threat model: a document describing its architecture, attacker capabilities, trust boundaries, and potential security failures. The result is a Markdown reference grounded in the repository's actual code and scope.

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• Documenting architecture and trust boundaries
• Creating or updating a repository threat model
• Preparing a threat model for a specific security review

View which models Weave Router can choose, grouped by provider. Enable or disable individual models or entire providers, set a preference order, and review the updated selection without restarting the router.

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• Review available models by provider
• Enable or disable specific models
• Enable or disable an entire provider

Check whether Kiln’s base models still support fine-tuning, the process of adapting a model with additional training. Compare configured identifiers and Fireworks’ allowlist with provider information, then report unsupported entries and recommend specific updates.

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• Audit support for fine-tuning base models in Kiln
• Check whether configured fine-tuning identifiers remain valid
• Find stale entries in the Fireworks allowlist