Plugins
2 pluginsResults for “embedding”
20 skills
orchestra-research sentence-transformers
Generate high-quality sentence and text embeddings for semantic similarity, clustering, and retrieval using 5000+ pre-trained models. Supports multilingual and domain-specific embeddings for RAG and semantic search.
10.4k · bundle
nvidia nemotron-retrieval-recipes
Plan, debug, tune, evaluate, export, or deploy public Nemotron embedding and reranking retrieval recipes using the current checkout.
2.2k · bundle
drnabeelkhan ai-engineer
Implements machine learning models, embeddings, and AI-powered features with ethical considerations, including model selection, integration, and monitoring.
2
jorcan llm-ops
Guides production LLM operations: RAG pipelines, embeddings, vector databases, fine-tuning, prompt engineering, cost estimation, quality evals, and AI architectures.
0 · bundle
oyi77 rag-builder
Designs and implements RAG pipelines, covering document chunking, embedding strategies, hybrid search, answer synthesis with source attribution, and evaluation using RAGAS metrics.
10
phoroth llm-ops
Implements production LLM operations: RAG pipelines, embeddings, vector databases, fine-tuning, advanced prompt engineering, cost estimation, quality evals, semantic caching, streaming, and agents.
3
More results
nvidia tao-finetune-cosmos-embed
Fine-tune, evaluate, run inference, and export Cosmos-Embed1 video-text embedding models for tasks like text-to-video retrieval and semantic deduplication.
2.2k · bundle
majiayu000 esm
Generates and analyzes proteins using ESM3 and ESM C language models, covering sequence generation, structure prediction, inverse folding, embeddings, and function conditioning with local or cloud-based Forge API inference.
567 · bundle
qhjqhj00 tensorboard
Visualize training metrics, debug models with histograms, compare experiments, visualize model graphs, and profile performance with TensorBoard.
3 · bundle
qhjqhj00 reflex
Evaluates machine-generated log summaries without human-written references, using LLM judgment and dense embeddings to score relevance, informativeness, and coherence.
3
qhjqhj00 umap-learn
Reduce high-dimensional data with UMAP for visualization, clustering preprocessing, and supervised or semi-supervised learning, including parameter tuning guidance.
3 · bundle
nvidia tao-train-reid
Trains, evaluates, exports, and runs inference for person re-identification models using TAO, learning discriminative embeddings for cross-camera matching.
2.2k · bundle
qhjqhj00 flops
Evaluates computational throughput and real-time efficiency of embedded CPU and GPU platforms by measuring peak FLOPS via a matrix rotation kernel and assessing inference latency and power consumption on a robotic vision pipeline.
3
lingxling geniml
Trains machine learning models on genomic interval data from BED files, including region embeddings, single-cell ATAC-seq analysis, and consensus peak building.
253 · bundle
qhjqhj00 hare
Computes the HARE Score, an entity- and relation-centric metric for evaluating machine-generated histopathology reports against ground truth, using GatorTronS+SapBERT embeddings and relation F1.
3
k-dense-ai geniml
Train unsupervised machine learning models on genomic interval data from BED files, including region embeddings, single-cell ATAC-seq analysis, and consensus peak building.
30.2k · bundle
qhjqhj00 stream
Evaluates spatial realism and temporal flow consistency of AI-generated videos using embedding spaces and Fourier transforms, producing bounded STREAM-S and STREAM-T scores.
3
qhjqhj00 tpr-fpr
Evaluates speaker verification models by computing true positive rate at fixed false positive rate thresholds, probing embedding space separation of same-speaker versus different-speaker pairs.
3
nvidia tao-finetune-clip
Fine-tune and deploy CLIP vision-language models for zero-shot classification, image-text retrieval, and embedding extraction with ONNX and TensorRT support.
2.2k · bundle
huggingface hf-cloud-sagemaker-deployment-planner
Plans and coordinates the deployment of a model to Amazon SageMaker AI, selecting the appropriate pathway (real-time, serverless, async, batch, or Bedrock CMI) based on model type, traffic, latency, and cost constraints.
10.8k