by K-Dense-AI
The hugging-science skill helps you find and use scientific AI resources from the Hugging Science catalog and the `hugging-science` Hugging Face org. It fits biology, chemistry, climate, genomics, materials, astronomy, and similar work when you need a dataset, model, Space, or blog post you can actually run or cite. Use it for hugging-science usage and hugging-science guide workflows instead of generic search.
by huggingface
Use transformers-js to run ML models in JavaScript and TypeScript across browser and server runtimes. The transformers-js skill covers install, model loading, caching, configuration, and practical transformers-js usage for text, vision, audio, multimodal tasks, and transformers-js for Code Generation with supported text-generation models.
by huggingface
huggingface-vision-trainer helps you install and use a Hugging Face skill for vision training jobs: object detection, image classification, and SAM/SAM2 segmentation. It covers dataset prep, cloud GPU setup, evaluation, Trackio logging, and pushing results to the Hub. Ideal for backend automation and repeatable training workflows.
by huggingface
huggingface-llm-trainer helps you train or fine-tune language and vision models on Hugging Face Jobs with TRL or Unsloth. Use this huggingface-llm-trainer skill for SFT, DPO, GRPO, reward modeling, dataset checks, GPU selection, Hub saving, Trackio monitoring, and GGUF export for backend development workflows.
by huggingface
huggingface-community-evals helps you run Hugging Face Hub model evaluations locally with inspect-ai or lighteval. Use it for backend selection, smoke tests, and a practical guide to vLLM, Transformers, or accelerate. Not for HF Jobs orchestration, model-card PRs, .eval_results publishing, or community-evals automation.
by K-Dense-AI
The transformers skill helps you use Hugging Face Transformers for model loading, inference, tokenization, and fine-tuning. It is a practical transformers guide for Machine Learning tasks across text, vision, audio, and multimodal workflows, with clear paths for quick baselines and custom training.
