'nemo' 검색 결과

Stage 4 of the Clinical ASR Flywheel. Use when priority KER is above 0.3 to run stock NeMo SFT on Parakeet TDT v2 and offline cycle N+1 re-eval. NOT for generic word boosting (use /finetune-asr).

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Generate and analyze DNA sequences using NVIDIA's Evo 2 BioNeMo NIM microservice. Use for Evo2/Evo 2, DNA generation, genomic sequence generation, hosted generation, local Docker deployment, local forward passes, layer outputs, logits, sampled probabilities, and BioNeMo NIM workflows.

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NOTE: molecule and target inputs and your NGC_API_KEY are transmitted to external NVIDIA-hosted API endpoints on every call. Use local NIM containers for confidential or proprietary data. Run a complete computational drug discovery pipeline using NVIDIA BioNeMo NIMs: generate drug-like molecules with GenMol, dock them to a protein target with DiffDock, then predict binding affinity with Boltz2. Use this skill whenever the user wants to generate and screen small molecule drug candidates, perform hit discovery, optimize leads against a protein target, or do virtual screening combining molecule generation, docking, and affinity prediction. Triggers on: drug discovery pipeline, hit discovery, lead optimization, virtual screening, molecule generation, molecular docking, binding affinity, GenMol, DiffDock, Boltz2, SMILES, SAFE notation, NIM microservice. This is a multi-step pipeline composing three BioNeMo NIMs.

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NVIDIA NeMo Guardrails configuration for conversational safety and control

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Vue 3 form patterns: native v-model, vee-validate + zod (most common), VueUse helpers, defineModel for custom inputs, controlled vs uncontrolled, field arrays, multi-step wizards. Use this skill to: - Wire vee-validate with a validation schema (zod / yup). - Build custom form components with defineModel (Vue 3.4+). - Implement multi-step forms. - Handle async validation. - Integrate forms with TanStack Query mutations or Pinia actions. Do NOT use this skill for: - General SFC conventions (see vue-conventions). - State management (see vue-state-management). - Routing (see vue-routing). - Testing forms (see vue-testing).

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Generates NeMo Guardrails Colang (.co) files and YAML config blocks from a plain-language description of a chatbot's purpose, allowed behaviors, and constraints. Use this skill whenever a user wants to build guardrails for a chatbot, define allowed intents for an LLM, create an AI firewall with NeMo Guardrails, generate Colang flow definitions, or configure a semantic allow-list for a bot. Trigger this skill even when the user just describes what their bot should and shouldn't do — generating the Colang and YAML is almost always what they need next.

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Upgrade the vendored NeMo-Skills slice to a newer upstream commit. Use when the user asks to "bump vendored", "sync NeMo-Skills", "update vendored sha", "upgrade NS", or wants to pull a specific upstream fix. Inside the sgl-eval repo.

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Framework de segurança em runtime da NVIDIA para aplicações LLM. Com detecção de jailbreak, validação de entrada/saída, verificação de fatos, detecção de alucinações, filtragem de PII, detecção de toxicidade. Usa DSL Colang 2.0 para rails programáveis. Pronto para produção, executa em GPU T4.

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Avalia LLMs em 100+ benchmarks de 18+ harnesses (MMLU, HumanEval, GSM8K, segurança, VLM) com execução multi-backend. Use quando precisar de avaliação escalável em Docker local, HPC Slurm ou plataformas cloud. Plataforma enterprise-grade da NVIDIA com arquitetura container-first para benchmarking reproduzível.

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Curação de dados acelerada por GPU para treinamento de LLM. Suporta texto/imagem/vídeo/áudio. Recursos incluem deduplicação fuzzy (16× mais rápida), filtragem de qualidade (30+ heurísticas), deduplicação semântica, redação de PII, detecção NSFW. Escala em GPUs com RAPIDS. Use para preparar datasets de treinamento de alta qualidade, limpar dados web ou deduplicar grandes corpora.

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Deploy Llama Guard, NeMo Guardrails, and LLM Guard input/output scanners as runtime defenses.

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Implements input and output validation guardrails for LLM-powered applications to prevent prompt injection, data leakage, toxic content generation, and hallucinated outputs. Builds a security validation pipeline using NVIDIA NeMo Guardrails Colang definitions, custom Python validators for PII Tespit and content policy enforcement, and the Guardrails AI framework for structured output validation. The guardrails system intercepts both user inputs (blocking injection attempts, stripping PII, ...

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GPU-accelerated data curation for LLM training. Supports text/image/video/audio. Features fuzzy deduplication (16× faster), quality filtering (30+ heuristics), semantic deduplication, PII redaction, NSFW detection. Scales across GPUs with RAPIDS. Use for preparing high-quality training datasets, cleaning web data, or deduplicating large corpora.

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Evaluates LLMs across 100+ benchmarks from 18+ harnesses (MMLU, HumanEval, GSM8K, safety, VLM) with multi-backend execution. Use when needing scalable evaluation on local Docker, Slurm HPC, or cloud platforms. NVIDIA's enterprise-grade platform with container-first architecture for reproducible benchmarking.

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Performs a comprehensive security review of code changes in a GitHub PR or issue. Checks out the branch, analyzes changed files against a 9-category security checklist, and produces PASS/WARNING/FAIL verdicts. Use when reviewing pull requests for security vulnerabilities, hardcoded secrets, injection flaws, auth bypasses, or insecure configurations. Trigger keywords - security review, code review, appsec, vulnerability assessment, security audit, review PR security.

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Scan recent git commits for changes that affect user-facing behavior, then draft or update the corresponding documentation pages. Use when docs have fallen behind code changes, after a batch of features lands, or when preparing a release. Trigger keywords - update docs, draft docs, docs from commits, sync docs, catch up docs, doc debt, docs behind, docs drift.

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Cut a new semver release — bump all version strings via bump-version.ts, open a release PR, and after merge tag main and push. Use when cutting a release, tagging a version, shipping a build, or preparing a deployment. Trigger keywords - cut tag, release tag, new tag, cut release, tag version, ship it.

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Finds open GitHub PRs with security and priority-high labels, links each to its issue, detects duplicates (multiple PRs fixing the same issue), and presents a table of review candidates. Use when looking for the next PR to review. Trigger keywords - find pr, find review, next pr, pr to review, duplicate pr, security pr.

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GPU-accelerated data curation for LLM training. Supports text/image/video/audio. Features fuzzy deduplication (16× faster), quality filtering (30+ heuristics), semantic deduplication, PII redaction, NSFW detection. Scales across GPUs with RAPIDS. Use for preparing high-quality training datasets, cleaning web data, or deduplicating large corpora.

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Vue.js 3와 Nuxt 화면 구현을 컴포넌트 설계부터 상태 관리, 화면 이동, 테스트까지 지원합니다. 구체적인 예제로 코드 구성과 데이터 갱신 방식을 검토합니다.

• Vue 컴포넌트 구성 검토하기
• 컴포넌트 간 재사용 로직 분리하기
• Pinia로 공유 상태 설계하기

Plan security controls for LLM applications and AI agents, covering malicious instructions, data leakage, model tampering, and API access. Includes guidance and code examples for filtering outputs, enforcing permissions, isolating networks, and recording audit events.

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• Reviewing defenses against prompt injection in AI agents
• Filtering personal information from LLM responses
• Designing authentication and rate limits for LLM APIs

Guides the use of RWKV, a language model architecture that carries information forward in a compact state. Covers streaming text generation, long document processing, fine-tuning, and memory comparisons with Transformers.

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• Build streaming text generation
• Process long documents in chunks
• Compare memory needs with Transformers

Configure large language model training with NVIDIA Megatron-Core across multiple GPUs. This skill helps choose how to split computation, set training parameters, monitor GPU efficiency, and address memory limits or unstable training.

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• Planning multi-GPU training for a LLaMA-style model
• Distributing MoE model experts across GPUs
• Tuning training throughput and GPU utilization

Configure large language model training across multiple NVIDIA GPUs with Megatron-Core. The skill helps choose how to split models and workloads, set training parameters, monitor performance, and address memory shortages or low GPU utilization.

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• Planning distributed training for a LLaMA-style model
• Distributing MoE expert networks across GPUs
• Tuning GPU utilization and training throughput