'lms' 검색 결과

Searching internet for technical documentation using llms.txt standard, GitHub repositories via Repomix, and parallel exploration. Use when user needs: (1) Latest documentation for libraries/frameworks, (2) Documentation in llms.txt format, (3) GitHub repository analysis, (4) Documentation without direct llms.txt support, (5) Multiple documentation sources in parallel

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Frappe LMS customization guidance for courses, batches, lessons, quizzes, assessments, enrollment, progress, certificates, portals, and learning workflows. Use when work touches Frappe LMS or education/training flows.

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Fine-tune LLMs and VLMs using Unsloth on HF Jobs (Hugging Face on-demand cloud GPUs). Use when users want to fine-tune language models, train VLMs (Vision Language Models), do continued pretraining, domain adaptation, or run UV scripts on HF Jobs. Triggers on requests involving Unsloth training, HF Jobs GPU training, Qwen3-VL fine-tuning, Gemma VLM training, or LoRA fine-tuning on cloud GPUs.

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Analyzes and generates llms.txt files -- the emerging standard for helping AI systems understand website structure and content. Can validate existing llms.txt files or generate new ones from scratch by crawling the site.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Short alias for /llms: settle the current question through a targeted, bounded consensus of other LLMs. Use when you want outside models to agree or disagree with a plan before acting. All logic lives in the llms skill. Triggers: /LLLL, llll.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

/LLMs решает текущий вопрос через ограниченный консенсус Claude, Codex, Cursor, Grok и других пиров. Передаёт тему и контекст сессии, читает только адресные сообщения этой темы, требует доказательства и закрывает ACCEPT или BLOCK. Триггеры: /LLMs, договорись с другими LLM, найди консенсус моделей

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Technical SEO for AI search engines. Checks AI crawler accessibility, llms.txt compliance, robots.txt configuration, server-side rendering. Outputs problems + fixes (generates llms.txt, robots.txt modifications). Use when user says "AI crawlers", "llms.txt", "GPTBot", "GEO technical".

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Creates an llms.txt file from scratch based on repository structure. Use when configuring LLM discovery files. For updating existing configurations, see update-llms.

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Updates the llms.txt file to reflect changes in documentation. Use when editing repository details or specifications. For creating from scratch, see create-llms.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

공통 과제로 대규모 언어 모델의 지식·추론·코드 생성을 평가합니다. 모델 비교표와 학습 단계별 성적 추이를 만들고 평가 결과와 개별 답변을 저장합니다.

• MMLU와 GSM8K로 모델 평가
• 여러 모델의 성적 비교표 작성
• 학습 중 모델의 성적 추적

Ant Design 공식 문서에서 React 컴포넌트의 설정 항목, 기본값, 사용 예시를 찾습니다. 하위 컴포넌트와 메서드, TypeScript 타입 정의도 확인합니다.

• 컴포넌트 설정 항목과 기본값 확인
• 사용 예시와 데모 코드 검색
• 하위 컴포넌트와 메서드 확인

Compress LLM weights using HQQ quantization, which stores weights with fewer bits without calibration datasets. Follow workflows for precision settings, model saving, inference integration, and LoRA fine-tuning of quantized models.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compressing LLMs without calibration datasets
• Serving quantized models with vLLM or HuggingFace Transformers
• Comparing bit widths and inference backends

Use LitGPT to explore language model implementations and train models on custom data. Covers pretrained model loading, LoRA fine-tuning that updates a small set of parameters, pretraining, and preparing trained models for deployment.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Studying language model architecture through readable code
• Fine-tuning a model on custom instruction data
• Configuring LoRA training for a single GPU

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

Deploy language models with vLLM through an OpenAI-compatible API or offline batch processing. Tune response speed, throughput, and GPU memory use with quantization, which reduces numerical precision, and execution across multiple GPUs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Deploy an OpenAI-compatible language model API
• Generate text from many prompts in a batch
• Serve quantized models with limited GPU memory

Helps adapt LLMs to specific tasks with LLaMA-Factory, including training through a web interface and memory-saving methods such as QLoRA. Provides documentation references for learning features, implementing workflows, and investigating code problems.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Exploring LLM fine-tuning through a web interface
• Checking LoRA and QLoRA training options
• Investigating LLaMA-Factory implementation problems

Create and edit Kortix presentations as web pages, with diagrams revealed step by step and presenter scripts. The skill also covers product films rendered to MP4, plus keyboard controls, recording procedures, and quality checks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create a web presentation for a product walkthrough
• Explain a mechanism with step-by-step diagrams
• Prepare presenter scripts and recording

Update documentation to match source changes on the current branch. Map changed files to existing pages, verify behavior against the code, revise affected references and examples, and report missing documentation using repository-specific mapping rules.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Updating API documentation after code changes
• Fixing guides and examples affected by renamed parameters
• Finding missing documentation pages or sections

Deploy language model APIs and run batch generation with vLLM. Configure GPU memory use, quantization—lower precision model storage—and distribution across GPUs, then test response times and monitor how many requests the server handles.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Deploying an OpenAI-compatible language model API
• Generating responses for a large set of prompts
• Fitting a large model into limited GPU memory

Get guidance on adapting LLMs through additional training with LLaMA-Factory. The skill covers a visual interface requiring no code, LoRA and QLoRA training methods, and documentation-based help with implementation, features, and debugging.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Learning additional training through a visual interface
• Exploring LoRA and QLoRA training methods
• Checking supported models and multimodal features

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

Use LitGPT to explore language model implementations and train models on custom data. Covers pretrained model loading, LoRA fine-tuning that updates a small set of parameters, pretraining, and preparing trained models for deployment.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Studying language model architecture through readable code
• Fine-tuning a model on custom instruction data
• Configuring LoRA training for a single GPU

Check whether AI crawlers, the bots that collect web content for AI services, can access a website. Get a crawler access table, an assessment score, and recommendations based on robots.txt, page tags, HTTP headers, and rendering requirements.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking which AI crawlers can access a website
• Reviewing unintended blocks in robots.txt
• Investigating content that requires JavaScript to display

Crawl linked pages within a chosen website scope and collect clean text, Markdown, or HTML with page metadata. Use the resulting records as source material for AI search systems or chatbot knowledge bases.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Collect documentation as Markdown
• Prepare source material for a chatbot knowledge base
• Gather blog content within a chosen scope