Découvrez les skills tools machine learning, comparez leurs dépôts et consultez leurs instructions.
Guidance for finding the largest eigenvalue of small dense matrices with performance optimization. This skill should be used when tasks involve computing eigenvalues (especially the dominant/largest eigenvalue)...
Guidance for implementing CLI tools that perform inference using PyTorch models in native languages (C/C++/Rust). This skill should be used when tasks involve extracting weights from PyTorch .pth files, impleme...
Guidance for deploying HuggingFace models as inference APIs/services. This skill applies when tasks involve downloading pre-trained models from HuggingFace Hub, creating REST APIs for model inference, building...
Guidance for training FastText text classification models with constraints on model size and accuracy. This skill should be used when training FastText models, optimizing hyperparameters, or balancing trade-off...
Guidance for creating standalone CLI tools that perform neural network inference by extracting PyTorch model weights and reimplementing inference in C/C++. This skill applies when tasks involve converting PyTor...
Guidance for extracting weight matrices from black-box ReLU neural networks using only input-output queries. This skill applies when tasks involve model extraction attacks, recovering hidden layer weights from...
Guidance for querying ML model leaderboards and benchmarks (MTEB, HuggingFace, embedding benchmarks). This skill applies when tasks involve finding top-performing models on specific benchmarks, comparing model...
This skill provides guidance for implementing PyTorch pipeline parallelism for distributed training of large language models. It should be used when implementing pipeline parallel training loops, partitioning t...
Guidance for extracting weight matrices from black-box ReLU neural networks using only input-output queries. This skill applies when tasked with recovering internal parameters (weights, biases) of a neural netw...
Guidance for implementing neural network inference (like GPT-2) under extreme code size constraints. This skill should be used when tasks require implementing ML model inference in minimal code (code golf), par...
Guide for Bayesian Network tasks involving structure learning, parameter fitting, intervention, and sampling. This skill should be used when working with pgmpy or similar libraries to recover DAG structures fro...
Guidance for implementing PyTorch pipeline parallelism for distributed model training. This skill should be used when tasks involve implementing pipeline parallelism, distributed training with model partitionin...
Guidance for building Caffe from source and training CIFAR-10 models. This skill applies when tasks involve compiling Caffe deep learning framework, configuring Makefile.config, preparing CIFAR-10 dataset, or t...
Guidance for recovering PyTorch model architectures from state dictionaries, retraining specific layers, and saving models in TorchScript format. This skill should be used when tasks involve reconstructing mode...
This skill provides guidance for retrieving and verifying information from dynamic ML leaderboards (MTEB, Scandinavian Embedding Benchmark, HuggingFace leaderboards, etc.) with specific temporal requirements. I...
This skill provides guidance for translating RStan (R-based Stan interface) code to PyStan (Python-based Stan interface). It should be used when converting Stan models from R to Python, migrating Bayesian infer...
Guidance for building and training with the Caffe deep learning framework on CIFAR-10 dataset. This skill applies when tasks involve compiling Caffe from source, training convolutional neural networks on image...
This skill provides guidance for training FastText text classification models with constraints on accuracy and model size. It should be used when training fastText supervised models, optimizing model size while...
Guidance for setting up HuggingFace model inference services with Flask APIs. This skill applies when downloading HuggingFace models, creating inference endpoints, or building ML model serving APIs. Use for tas...
Guidance for Bayesian Network DAG structure recovery, parameter learning, and causal intervention tasks. This skill should be used when tasks involve recovering DAG structure from observational data, learning B...
This skill should be used when reconstructing PyTorch models from weight files (state dictionaries), checkpoint files, or partial model artifacts. It applies when the agent needs to infer model architecture fro...
This skill should be used when the user asks to "predictive intelligence", "machine learning", "ML", "classification", "similarity", "clustering", "prediction", "AI", or any ServiceNow Predictive Intelligence d...
Deploy production recommendation systems with feature stores, caching, A/B testing. Use for personalization APIs, low latency serving, or encountering cache invalidation, experiment tracking, quality monitoring...
Train ML models with scikit-learn, PyTorch, TensorFlow. Use for classification/regression, neural networks, hyperparameter tuning, or encountering overfitting, underfitting, convergence issues.
Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.