Browse Skills

Train large language models (7B-70B+) using RLHF techniques like PPO, GRPO, RLOO, and DPO with distributed Ray and vLLM acceleration, achieving 2× faster training than DeepSpeedChat.

Fine-tune large language models (7B-70B) using parameter-efficient methods like LoRA and QLoRA. Ideal when memory is limited, training only a fraction of parameters with minimal quality loss.

Prompt Guard detects prompt injection and jailbreak attempts in LLM apps. It can also screen third-party text such as API responses or RAG documents before they reach a model.

PyTorch FSDP2 adds fully_shard-based distributed sharding to training scripts with the right init, mixed precision, offload, optimizer, and checkpointing flow. Use it when a model does not fit on one GPU or you need DTensor-based sharding with DeviceMesh.

PyTorch Lightning organizes PyTorch training into LightningModule and Trainer abstractions with built-in logging, checkpoints, and distributed training. Use it when you want cleaner training loops and the same code to run from a laptop to a multi-GPU system.

Provides guidance for performing causal interventions on PyTorch models using pyvene. Use for causal tracing, activation patching, interchange intervention training, or testing causal hypotheses about model behavior.

Ray Data is a distributed data processing library for ML workloads, supporting streaming execution on CPUs and GPUs. It handles Parquet, CSV, JSON, and images, and integrates with PyTorch, TensorFlow, and Ray Train for preprocessing, batch inference, and ETL.

Ray Train orchestrates distributed machine learning training across single-GPU and multi-node clusters. Use it to scale PyTorch, TensorFlow, or HuggingFace training and run hyperparameter sweeps with fault tolerance.

Implement and deploy the RWKV architecture, a hybrid RNN-Transformer with linear-time inference, infinite context, and no KV cache. Ideal for long-context processing, streaming generation, and memory-constrained deployment.

SentencePiece is a language-independent tokenizer for multilingual models and CJK languages. It supports BPE and Unigram algorithms, with fast processing (50k sentences/sec) and deterministic vocabulary.

Simple Preference Optimization for LLM alignment. Reference-free alternative to DPO with better performance (+6.4 points on AlpacaEval 2.0). No reference model needed, more efficient than DPO. Use for preference alignment when want simpler, faster training than DPO/PPO.

SkyPilot Multi-Cloud Orchestration runs ML training, batch jobs, and serving across multiple clouds with automatic cost-based placement. Use it for spot instances, auto-recovery, distributed training, and avoiding vendor lock-in.