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  • Use when planning, debugging, tuning, evaluating, exporting, or deploying public Nemotron `embed`/`rerank` retrieval recipes.
    Skill

  • Use when operating PAIDF Curation and Retrieval or NVIDIA Cosmos Curator pipelines (split, filter, caption, embed, dedup, shard, image annotate) or PAIDF Data Mining nearest-neighbor matching on Curator embeddings. Activate for Make or CLI pipeline config
    Skill
  • NVIDIA
    Free Endpoint

    nemotron-3-embed-1b

    1B embedding model for semantic search, retrieval, and RAG applications.
    Model
  • General
    LaunchableDeveloper Example

    Build a RAG Pipeline Blueprint

    Power fast, accurate semantic search across multimodal enterprise data with NVIDIA’s RAG Blueprint—built on NeMo Retriever and Nemotron models—to connect your agents to trusted, authoritative sources of knowledge.
    Blueprint
  • Meta
    DownloadableFree Endpoint

    llama-3.2-11b-vision-instruct

    Cutting-edge vision-language model exceling in high-quality reasoning from images.
    Model
  • Meta
    DownloadableFree Endpoint

    llama-3.2-90b-vision-instruct

    Cutting-edge vision-Language model exceling in high-quality reasoning from images.
    Model
  • Retail
    LaunchableDeveloper Example

    Multi-Agent Intelligent Warehouse

    An AI-powered, multi-agent system designed to optimize warehouse operations through intelligent automation, real-time monitoring, and natural language interaction.
    Blueprint
  • Retail
    LaunchableDeveloper Example

    Retail Shopping Assistant

    Elevate Shopping Experiences Online and In Stores.
    Blueprint
  • NVIDIA
    Downloadable

    llama-nemotron-embed-vl-1b-v2

    Multimodal question-answer retrieval representing user queries as text and documents as images.
    Model

  • Run iterative improvement for NVIDIA TAO CLIP / SigLIP image-text retrieval on attribute-labelled data. Use when a request combines retrieval evaluation, weak-attribute or caption-pair mining, repeated retraining, and a stopping condition based on a retri
    Skill
  • Physical AI & Robotics
    Enterprise

    Cosmos Dataset Search

    Accelerate post-training of end-to-end autonomous vehicle stacks with vector search and retrieval for large video datasets.
    Blueprint
  • General
    Developer Example

    Nsight Copilot - AI Code Assistant for CUDA Development

    Deploy an AI-powered coding assistant on DGX Spark that delivers expert CUDA-aware chat, real-time code completion, and retrieval-augmented generation grounded in authoritative GPU programming knowledge—powered by NVIDIA NIM microservices.
    Blueprint

    • Use when running video data augmentation and auto-labeling workflows on OSMO: flow selection, preflight, submit-time interpolation, monitoring, and output retrieval. Trigger keywords: video data augmentation, data enrichment, auto labeling, VDA demo, OSMO
      Skill

    • CLIP vision-language model for image-text retrieval, zero-shot classification, embedding extraction, ONNX export, and TensorRT deployment. Use when fine-tuning or training CLIP, running zero-shot classification, computing image embeddings, or deploying CL
      Skill

    • Cosmos-Embed1 video-text embedding for text-to-video retrieval, video-to-video search, semantic deduplication, and fine-tuning. Use when the user asks to "fine-tune Cosmos-Embed1", "run cosmos-embed inference", "export Cosmos-Embed1", "embed videos", or "
      Skill

    • NV-Tesseract Forecasting — transformer-based multivariate time series forecasting with DARR (context-enhanced kNN retrieval), interpretability, and fine-tuning. Use when the user asks to "forecast with NV-Tesseract", "run forecasting inference", "use perf
      Skill

    • InternVideo2-CLIP L14 (TAO video_clip) for video-text retrieval, zero-shot classification, embedding extraction, LoRA fine-tuning, ONNX export, and TensorRT deployment. Use when the user asks to "fine-tune IV2CLIP", "run video_clip train/evaluate/inferenc
      Skill

    • Metric-learning recognition (ml-recog) for fine-grained visual recognition. Learns embeddings for retrieval-based matching (e.g., retail product recognition) using triplet / contrastive losses. Use when training, evaluating, exporting, or running inferenc
      Skill
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