AI Skills for Hugging Face

Evaluates LLMs using 60+ benchmarks for model quality assessment, widely adopted in academic and industry settings.

Enables zero-shot image segmentation using Meta AI's Segment Anything Model, allowing flexible object detection in various domains.

Enables fast, calibration-free quantization of LLMs to 4/3/2-bit precision, optimizing memory and inference efficiency.

Generates high-quality images from text prompts using Stable Diffusion models, enabling creative workflows and image transformations.

LlamaGuard provides advanced content moderation for LLMs, ensuring safe interactions by filtering harmful inputs and outputs with high accuracy.

Mamba architecture offers a state-space model with O(n) complexity for efficient sequence modeling, enabling faster inference and long context handling.

Facilitates Hugging Face Hub operations via CLI for managing models, datasets, and repositories efficiently.

Enables tracking and visualization of ML training experiments with real-time dashboards and alerts for enhanced diagnostics.

Facilitates the management of evaluation results in Hugging Face model cards, enhancing model benchmarking and analysis.

Enables training and fine-tuning of language models on Hugging Face Jobs using TRL methods, optimizing for cloud GPU resources.

Trains and fine-tunes vision models for object detection and image classification using Hugging Face's cloud GPUs.

Connects AI assistants to Hugging Face Hub for model searches, dataset retrieval, and running compute jobs efficiently.

Facilitates dataset management on Hugging Face Hub with SQL querying, updates, and multi-format support for diverse data types.

Enables researchers to publish and manage papers on Hugging Face Hub, linking them to models and datasets seamlessly.

Enables users to search, download, and manage datasets from various repositories, enhancing data accessibility for machine learning projects.

Enhances content safety with two-layer moderation for user input and output, detecting prompt injections and sensitive topics.

Provides tools for speaker separation, voice comparison, and audio processing, enhancing voice cloning and verification tasks.

Generates diverse AI content like images, videos, and audio using HuggingFace Spaces, supporting batch and chained generation.

Evaluates LLMs using 60+ benchmarks for model quality assessment and comparison, widely adopted in academic and industry settings.

Enables fast, calibration-free quantization of LLMs to 4/3/2-bit precision, optimizing model performance without dataset requirements.