Agent Skills

trl-training

✓★ 18,774

by huggingface

Train and fine-tune transformer language models using TRL (Transformers Reinforcement Learning). Supports SFT, DPO, GRPO, KTO, RLOO and Reward Model training via CLI commands.

🧰 Not standalone — ships with the tool in huggingface/trl

cuda-kernels

✓★ 704

by huggingface

Provides guidance for writing and benchmarking optimized CUDA kernels for NVIDIA GPUs (H100, A100, T4) targeting HuggingFace diffusers and transformers libraries. Kernels must be kernel-builder/ABI3-compliant: no pybind11, no setup.py, TORCH_LIBRARY_EXPAND bindings only. Supports models like LTX-Video, Stable Diffusion, LLaMA, Mistral, and Qwen. Includes integration with HuggingFace Kernels Hub (get_kernel) for loading pre-compiled kernels. Includes benchmarking scripts to compare kernel performance against baseline implementations.

🧰 Not standalone — ships with the tool in huggingface/kernels

cpu-kernels

✓★ 704

by huggingface

Provides guidance for writing, optimizing, and benchmarking C++ CPU kernels with SIMD intrinsics (AVX2/AVX512) for the Hugging Face kernels ecosystem. Includes a two-phase workflow: Phase 1 correctness (generic → AVX2) and Phase 2 performance exploration (AVX512 with branching trial loop), runtime CPU dispatch, OpenMP threading, and brgemm integration for GEMM-heavy kernels.

🧰 Not standalone — ships with the tool in huggingface/kernels

xpu-kernels

✓★ 704

by huggingface

Provides guidance for writing, optimizing, and benchmarking Triton kernels for Intel XPU GPUs (Battlemage/Arc Pro B50) using the Xe-Forge optimization framework. Includes an LLM-driven trial-loop workflow (analyze, validate, benchmark, profile, finalize), XPU-specific patterns (tensor descriptors, GRF mode, tile swizzling), KernelBench fused kernels, and Flash Attention.

🧰 Not standalone — ships with the tool in huggingface/kernels

rocm-kernels

✓★ 704

by huggingface

Provides guidance for writing and benchmarking optimized Triton kernels for AMD GPUs (MI355X, R9700) on ROCm, targeting HuggingFace diffusers (LTX-Video, SD3, FLUX) and transformers. Core kernels: RMSNorm, RoPE 3D, GEGLU, AdaLN. Includes XCD swizzle, autotune, diffusers integration patterns, and LTX-Video pipeline injection.

🧰 Not standalone — ships with the tool in huggingface/kernels

inference-server

by huggingface

Start and test the prime-rl inference server. Use when asked to run inference, start vLLM, test a model, or launch the inference server.

🧰 Not standalone — ships with the tool in huggingface/prime-rl

installation

by huggingface

How to install prime-rl and its optional dependencies. Use when setting up the project, installing extras like deep-gemm for FP8 models, or troubleshooting dependency issues.

🧰 Not standalone — ships with the tool in huggingface/prime-rl

toml-config

by huggingface

How to write and use TOML configs in prime-rl. Use when creating config files, running commands with configs, or overriding config values via CLI.

🧰 Not standalone — ships with the tool in huggingface/prime-rl