Plugins

3 plugins

SQL, analytics, datasets, models and machine-learning workflows.

29 skills · plugin

Deploy Azure ML Pipeline

Manage Azure Machine Learning resources including workspaces, jobs, models, data, compute, and pipelines using the SDK v2 for Python.

3 skills · plugin

@alirezarezvani

Per-deal-and-packaging Commercial domain. v2.8.0 ships 8 skills: orchestrator + pricing-strategist (model picker + Van Westendorp WTP + packaging) + deal-desk (deal scorer + discount approval routing + redline) + partnerships-architect (5-tier classifier + joint GTM + revshare modeler) + channel-economics (cost-to-serve + ROI + mix optimizer) + commercial-policy (data-backed discount matrix + exce

8 skills · plugin

Results for “data-model”

43 skills

Detecting Data And Model Poisoning

Detect poisoned training data and backdoored models across the ML pipeline using statistical analysis, activation clustering, and spectral signatures.

24.6k · bundle

Turns model work into a production ML system with data contracts, reproducible training, quality gates, deployable artifacts, and monitoring.

sakamoto-family-smile

Turn model work into a production ML system with data contracts, reproducible training, quality gates, deployable artifacts, and monitoring.

Fine-tune models on Azure AI Foundry using SFT, DPO, or RFT, covering dataset preparation, training job submission, deployment, and evaluation.

Manage Hugging Face Hub resources: download/upload models, datasets, spaces; manage repos, buckets, collections, discussions, and cache; run SQL queries on datasets; authenticate and manage tokens.

Ml Adoption Playbook

Provides an adaptive methodology for adding machine learning models to existing codebases, covering problem framing, data readiness, architectural decoupling, and baseline model integration.

More results

Guides users through participating in the SOMA decentralized training network, covering data submission, model training, reward claiming, and strategic optimization.

Huggingface Hub

Operate Hugging Face Hub repositories, models, datasets, and Spaces via the hf CLI, including downloads, uploads, authentication, and compute jobs.

Manage Hugging Face Hub resources via the `hf` CLI: download and upload models, datasets, and spaces; manage buckets, cache, collections, discussions, and inference endpoints; run SQL queries on datasets.

Turn model work into a production ML system with data contracts, repeatable training, measurable quality gates, deployable artifacts, and operational monitoring.

Agent Platform Tuning

Fine-tune open models or Gemini models using Agent Platform infrastructure, from environment setup through data preparation, job configuration, monitoring, and deployment.

14.4k · bundle

Fit statistical models (OLS, GLM, ARIMA, mixed models) with detailed diagnostics, residuals, and inference for econometrics and time series analysis.

30.2k · bundle

Enforces rigorous ML modeling, feature engineering, training, and evaluation standards at principal-engineer level.

Search models, datasets, Spaces, and papers on Hugging Face Hub, retrieve repository details and documentation, run compute jobs, and use Gradio Spaces as AI tools via MCP server tools.

Fine-tune Gemma models locally using QLoRA, Unsloth, or TRL for SFT, DPO, and reward modeling, with dataset preparation and conversion to GGUF or LiteRT-LM.

Build and deploy machine learning models with TensorFlow, covering Keras, data pipelines, and production serving.

Connects AI assistants to the Hugging Face Hub via MCP server tools to search models, datasets, Spaces, and papers, retrieve repo details and documentation, run compute jobs, and use Gradio Spaces as AI tools.

Fine-tune instruction-following LLMs with Unsloth's optimized SFTTrainer, covering dataset formatting, chat templates, training configuration, and thinking-model patterns.

Connects AI assistants to the Hugging Face Hub via MCP server tools to search models, datasets, Spaces, and papers, retrieve repository details and documentation, run compute jobs, and use Gradio Spaces as AI tools.

Tao Finetune Huggingface Model

Fine-tune HuggingFace CV, VLM, or LLM models on local NVIDIA GPUs using an NGC PyTorch container, with support for full or LoRA training, dataset handling, and optional model push to the Hub.

leandrobenjaminl

Entrena modelos de machine learning con Scikit-learn, LightGBM y XGBoost, desde un baseline hasta un modelo productivo con validación robusta y explicabilidad.

Trains, evaluates, exports, and runs inference for person re-identification models using TAO, learning discriminative embeddings for cross-camera matching.

Earth2studio Create Diagnostic

Create Earth2Studio diagnostic model wrappers for single-step data transformations, including simple derived diagnostics, packaged AutoModel diagnostics, and generative or diffusion diagnostics.

Trains language models with Direct Preference Optimization using preference pairs, covering DPOTrainer setup, dataset preparation, and beta tuning for stable preference learning without explicit reward models.

franks-intellab

Agentic Kaggle Skill

End-to-end Kaggle competition workflow for scored submissions, covering code competitions, validation, metrics, public notebook/discussion intel, tabular/text/image modeling, tuning, ensembling, multi-notebook architectures, Kaggle GPU offload, and hidden-test debugging.

Fine Tuning Expert

Fine-tune LLMs using LoRA, QLoRA, and PEFT with Hugging Face, including dataset preparation, hyperparameter tuning, evaluation, and deployment.

10.4k · bundle

Book Sft Pipeline

Convert books into supervised fine-tuning datasets and train style-transfer models that replicate an author's voice.

16.9k · bundle

Reads and preprocesses 5-minute stock candlestick CSV data, then clusters the time series using tslearn's TimeSeriesKMeans, including data cleaning, percentage change calculation, model training, saving, and representative sample extraction.

orchestra-research

Hqq Quantization

Quantize large language models to 8/4/3/2/1-bit precision without calibration data, using multiple optimized backends for deployment with vLLM or HuggingFace Transformers.

10.4k · bundle

Pytorch Patterns

Provides idiomatic PyTorch patterns and best practices for building robust, efficient, and reproducible deep learning applications, covering model architecture, training loops, data pipelines, and checkpointing.

Agent Platform Eval Flywheel

Measures and improves the quality of AI models and agents on Google Cloud using the Eval Quality Flywheel methodology, including dataset creation, metric selection, failure analysis, and iterative improvement.

14.4k · bundle

Huggingface Vision Trainer

Trains and fine-tunes vision models for object detection, image classification, and segmentation using Hugging Face Transformers on cloud GPUs, with automatic dataset validation and Hub persistence.

10.8k · bundle

Physicsnemo Discover

Navigate the PhysicsNeMo repository by discovering model families, datapipes, and examples through live file search, without writing training code.

Tao Run Deft Aoi

Automates the full DEFT AOI improvement loop for NVIDIA TAO VisualChangeNet / ChangeNet PCB inspection models, including baseline evaluation, RCA, synthetic defect generation, data mining, retraining, and deployment gating until KPI targets are met.

Trains machine learning models on genomic interval data from BED files, including region embeddings, single-cell ATAC-seq analysis, and consensus peak building.

Hqq Quantization

Quantize large language models to 8/4/3/2/1-bit precision without calibration data, using multiple optimized backends and integrations with HuggingFace Transformers, vLLM, and PEFT/LoRA.