'attention' 검색 결과

Set up LLM serving with structured responses, including JSON and outputs restricted to specified patterns. Covers reusing shared prompt calculations, tool calling, and multi-turn conversations through SGLang, with examples for server setup and application integration.

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• Extract information into JSON with a defined schema
• Build agents that call tools
• Reuse shared prompts across chat requests

Build LLM serving workflows that produce JSON or other required formats. The skill covers reusing computation for shared prompts, tool calls, and ongoing conversations, with examples for server setup and structured output.

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• Extracting article details into JSON
• Building agents that call tools
• Reusing shared prompts across conversations

Implement paper-defined attention variants by extracting mechanism invariants, mapping tensor shapes, preserving module interfaces, validating numerical behavior, and integrating the custom attention into an existing transformer stack.

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Answer in the ADHD-friendly Attention-kind style for the rest of this chat.

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Get your attention back with a 30-day protocol that assumes you'll break it — a screen-time ledger without moralizing, friction engineering (what to delete, grayscale, where the phone sleeps), planned relapses, and honest replacement activities for the boredom that shows up on day 3. Use when someone says 'my screen time is 7 hours', 'I want a dumbphone', 'digital detox', 'I can't read books anymore', or 'my attention span is gone'. Produces the ledger, a personal friction plan, and the 30-day protocol with expected failure points.

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Guides Attention-Gated Networks PyTorch medical imaging workflows for ultrasound classification, attention-gated U-Net segmentation, data layout, CUDA setup, and visualization helpers.

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Review and adapt MetaX attention backends, MLA, sparse indexers, cache layouts, and their kernel wrappers against a target vLLM revision and the actually installed MetaX component APIs. Verify dispatch, supported configurations, and GPU numerical behavior. Use for attention adaptations outside vllm_metax/patch/; do not apply monkey-patch headers or patch audit requirements.

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Execute programs on a compiled transformer stack machine where every instruction fetch and memory read is a parabolic attention head. Demonstrates that transformer attention + FF layers can implement a working computer. Use when user mentions "llm-as-computer", "lac", "stack machine", "compiled transformer", "percepta", "parabolic attention", "execute program", or asks to run/trace programs on the transformer executor.

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Structured procedure for expanding attention from focused single-domain mode to panoramic multi-domain awareness. The cognitive transition from "focused attention on one problem" to "unfocused attention encompassing all relevant domains simultaneously." Like Baars' Global Workspace — consciousness as broadcast rather than spotlight. Use after meditation has cleared noise, when a problem spans multiple domains that need to be perceived together, when single-domain analysis keeps missing cross-domain connections, or as the opening move before integrate-gestalt synthesizes what is perceived.

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Analyze pre-trained generative diffusion models (Stable Diffusion, DALL-E, Flux) by computing quality metrics (FID, IS, CLIP score, precision/recall), inspecting noise schedules, extracting and visualizing attention maps, and probing latent spaces. Use when evaluating a pre-trained generative diffusion model's output quality, comparing noise schedule variants, analyzing cross-attention patterns for text-conditioned generation, interpolating between latent codes, or detecting out-of-distribution inputs.

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연결된 Paperclip 팀의 에이전트, 진행 중인 작업, 장애 요인과 승인 대기 사항을 확인합니다. 담당자가 대응할 내용을 작업 참조와 승인 링크를 붙여 정리합니다.

• 팀의 진행 상황 파악
• 장애 요인과 작업 불가 에이전트 확인
• 판단이 필요한 승인 대기 사항 확인

큰 글자와 여백, 빈칸 없는 카드 그리드, 스크롤에 연동되는 움직임을 조합해 데스크톱 랜딩 페이지를 시제품으로 만들고, 주목에서 행동으로 이어지는 구성을 설계합니다.

• 스크롤 효과가 있는 랜딩 페이지를 시제품으로 만듭니다
• 첫 화면의 배치와 글자 구성을 바꿉니다
• 빈칸 없는 카드 그리드를 설계합니다

서식과 레이아웃의 재현성을 고려해 .docx 문서를 읽고 새로 만들고 편집하도록 돕습니다. Word 문서의 내용과 외관을 함께 다루는 작업에 사용합니다.

• 기존 .docx 문서 읽기
• 서식이 있는 Word 문서 작성
• 문서의 외관을 유지하며 편집

claude-flow를 agentic-flow@alpha의 확장으로 재구성해 중복 구현을 줄입니다. 어댑터를 설계하고 기존 기능의 호환성을 확인하며 단계적으로 이전합니다.

• 에이전트·작업 관리의 중복 구현 정리
• 기존 API를 유지하는 어댑터 설계
• 학습·공유 메모리·MCP 도구 통합

claude-flow를 agentic-flow@alpha의 확장으로 재구성해 중복 구현을 줄입니다. 어댑터를 설계하고 기존 기능의 호환성을 확인하며 단계적으로 이전합니다.

• 에이전트·작업 관리의 중복 구현 정리
• 기존 API를 유지하는 어댑터 설계
• 학습·공유 메모리·MCP 도구 통합

Create a morning brief as a styled HTML page using connected calendars, email, and chat. It shows the day's meeting load, items needing attention, recent resolutions, and preparation for tomorrow, with an option for recurring weekday runs.

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• Getting a visual overview of today's schedule
• Checking unanswered requests that need attention today
• Finding concrete preparation for tomorrow's meetings

Review open GitHub issues for herdr and organize them into a concise Markdown table. Each row shows urgency, a recommended action, issue age, reactions, and a short reason to help decide what needs attention.

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• Deciding which open issues need immediate fixes
• Preparing a queue of feature requests and quality improvements
• Reviewing older issues and reports that need more evidence

Review findings, drafts, assessments, and reports left by different work desks in a shared folder. Use each desk’s journal for context, then summarize the current state and highlight items that need attention.

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• Review shared outputs at the start of a session
• Check another desk’s findings and context
• Review work before handing it to another desk

Apply consistent names to SGLang speculative decoding code, which verifies draft tokens to generate text. Distinguish tokens with or without the bonus token, counts, counters, and arrays when adding identifiers, renaming code, or reviewing changes.

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• Naming variables in speculative decoding code
• Renaming identifiers to match their meaning
• Reviewing metric and counter names

Explore marketing, publicity, and crisis communication through frameworks derived from Justin Sun’s public statements and actions. The skill examines attention, timing, and messaging, then responds in a forceful persona using verified figures and recognizable events.

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• Analyzing how publicity attracts attention
• Examining crisis communication narratives
• Considering when to join trending topics

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.

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

Helps speed up LLM text generation by comparing methods that predict and verify multiple tokens at once. Covers draft model selection, Medusa prediction heads, Lookahead Decoding settings, and deployment examples for inference services.

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• Reducing chatbot response latency
• Comparing methods for faster code generation
• Selecting a small draft model for a larger LLM

Extend the amount of text a language model can process at once. Compares RoPE, YaRN, ALiBi, and position interpolation, with implementation examples, additional training guidance, and memory optimization approaches for long document processing.

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• Extend an existing model to handle longer inputs
• Compare methods for representing token positions
• Plan additional training with long documents

Review unfinished work in Google Tasks by listing task lists and checking task due dates. Identify overdue items that need attention and prioritize them to decide which outstanding tasks to address first.

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• Find unfinished tasks that are past due
• Review due dates in a task list
• Prioritize overdue work