Browse Skills

Implements Group Relative Policy Optimization (GRPO) using TRL for fine-tuning language models with custom reward functions. Use for enforcing output formats, teaching verifiable tasks, improving reasoning, or aligning models without labeled preference data.

HQQ Quantization applies calibration-free half-quadratic weight quantization to LLMs at 8/4/3/2/1-bit precision. Use it when quantizing models quickly, deploying with vLLM or HuggingFace Transformers, or fine-tuning quantized models with LoRA.

HuggingFace Accelerate simplifies distributed PyTorch training with a small API and a single launch command. Use it to add multi-GPU, mixed precision, DeepSpeed, or FSDP support to one training script.

High-performance tokenization for NLP with Rust speed. Supports BPE, WordPiece, Unigram. Train custom tokenizers, handle batch encoding, padding, truncation, and alignment tracking. Integrates with Hugging Face Transformers. Use for fast tokenization or custom vocab training.

Implements and trains LLMs with LitGPT’s clean single-file model code. Use it for understanding architectures, fine-tuning with LoRA/QLoRA, or pretraining and deploying models.

Llama.cpp runs LLM inference locally on CPU, Apple Silicon, and non-NVIDIA GPUs with GGUF quantization. Use it for edge deployment, low-memory setups, or when CUDA is unavailable.

Expert guidance for fine-tuning LLMs using LLaMA-Factory, offering a no-code WebUI, support for over 100 models, and quantization with 2-8 bit QLoRA for multimodal applications.

Llama Guard filters LLM inputs and outputs for unsafe content across violence, sexual content, weapons, substances, self-harm, and criminal planning. Use it to block or review prompts and responses in production chat apps.

Llm Evaluation Harness evaluates LLMs across academic benchmarks like MMLU, GSM8K, HumanEval, and TruthfulQA. Use it to benchmark models, compare results, and track training progress.

Guide for using Mamba state-space models, offering O(n) complexity and 5× faster inference for long sequences. Ideal when you need efficient processing of millions of tokens without a KV cache.

Megatron-Core Training trains large language models with NVIDIA Megatron-Core using tensor, pipeline, context, and expert parallelism. Use it for distributed pretraining of models above 1B parameters on NVIDIA GPUs.

Provides guidance for enterprise-grade RL training using miles, a production-ready fork of slime. Use when training large MoE models with FP8/INT4, needing train-inference alignment, or requiring speculative RL for maximum throughput.