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llama-farm

The wt skill streamlines AI model deployment, enabling developers to work efficiently with isolated environments and parallel coding sessions, perfect for AI coding workflows

llama-farm

Deploy any AI model, agent, database, RAG, and pipeline locally or remotely in minutes

huggingface

Trains and fine-tunes vision models for object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm models — MobileNetV3, MobileViT, ResNet, ViT/DINOv3 — plus any Transformers classifier), and SAM/SAM2 segmentation using Hugging Face Transformers on Hugging Face Jobs cloud GPUs. Covers COCO-format dataset preparation, Albumentations augmentation, mAP/mAR evaluation, accuracy metrics, SAM segmentation with bbox/point prompts, DiceCE loss, hardware selection, cost estimation, Trackio monitoring, and Hub persistence. Use when users mention training object detection, image classification, SAM, SAM2, segmentation, image matting, DETR, D-FINE, RT-DETR, ViT, timm, MobileNet, ResNet, bounding box models, or fine-tuning vision models on Hugging Face Jobs.

goodnight000

End-to-end system for creating supervised fine-tuning datasets from books and training style-transfer models. Covers text extraction, intelligent segmentation, synthetic instruction generation, Tinker-compatible output, LoRA training, and validation.

hyperb1iss

Use this skill when building AI features, integrating LLMs, implementing RAG, working with embeddings, deploying ML models, or doing data science. Activates on mentions of OpenAI, Anthropic, Claude, GPT, LLM, RAG, embeddings, vector database, Pinecone, Qdrant, LangChain, LlamaIndex, DSPy, MLflow, fine-tuning, LoRA, QLoRA, model deployment, ML pipeline, feature engineering, or machine learning.