Skill UI

Hardware-agnostic quantum ML framework enabling gradient-based training of quantum circuits, hybrid quantum-classical models, and seamless integration with PyTorch/JAX/TensorFlow alongside device portability across IBM/Google/Rigetti/IonQ.

PufferLib is a high-performance library designed for advanced reinforcement learning simulations and training. It excels in parallel environment simulation, offering optimized vectorization and native support for multi-agent systems. Utilizing algorithms like PuffeRL (optimized PPO+LSTM), it achieves millions of steps per second, significantly accelerating research and deployment across single-agent and multi-agent environments.

Stable Baselines3 is a robust, PyTorch-based library providing production-ready implementations of core reinforcement learning algorithms (PPO, SAC, DQN, etc.). It facilitates the training of single-agent RL models, supports the creation of custom Gymnasium environments, and handles advanced parallel training via vectorized environments. It is ideal for rapid prototyping and rigorous RL experiments.

Train or fine-tune TRL-powered language models via Hugging Face Jobs, running SFT/DPO/GRPO workflows on managed cloud GPUs, auto-saving to the Hub and exporting GGUF builds without local setup.

Deploys and runs ML experiments on local, remote, Vast.ai, or Modal GPUs by detecting the target environment, syncing code, ensuring W&B logging when needed, and launching training jobs safely.

This skill acts as a crucial bridge, automating the transition from a conceptual experiment plan to fully deployed and executable code. It parses structured experiment roadmaps, implements necessary training/evaluation scripts, manages dependencies, and deploys the experiment setup to GPU environments. It ensures reproducibility through mandatory code review and structured result collection.

This skill automates the process of renting, configuring, and managing GPU instances on vast.ai for deep learning and AI tasks. It analyzes the task requirements (VRAM, GPU count, estimated time) from the experiment plan or user input, searches for the most cost-effective and reliable GPU offers, and manages the entire resource lifecycle, ensuring efficient and economic AI training.

Executes training, fine-tuning, inference, and batch jobs on Modal’s zero-config GPU cloud so you just write Python, run `modal run`, and let Modal auto scale, manage secrets, and halt billing when finishes.

Hardware-agnostic quantum ML framework enabling gradient-based training of quantum circuits, hybrid quantum-classical models, and seamless integration with PyTorch/JAX/TensorFlow alongside device portability across IBM/Google/Rigetti/IonQ.

PufferLib is a high-performance library designed for advanced reinforcement learning simulations and training. It excels in parallel environment simulation, offering optimized vectorization and native support for multi-agent systems. Utilizing algorithms like PuffeRL (optimized PPO+LSTM), it achieves millions of steps per second, significantly accelerating research and deployment across single-agent and multi-agent environments.

Stable Baselines3 is a robust, PyTorch-based library providing production-ready implementations of core reinforcement learning algorithms (PPO, SAC, DQN, etc.). It facilitates the training of single-agent RL models, supports the creation of custom Gymnasium environments, and handles advanced parallel training via vectorized environments. It is ideal for rapid prototyping and rigorous RL experiments.

This skill guides the secure handling and lifecycle management of large-scale data artifacts, including training datasets, model weights, and checkpoint snapshots, within GPU cloud environments. It provides best practices for data pipelines, covering secure import via Kubernetes PVCs, compliant export, and stringent data validation, ensuring adherence to encryption standards (AES-256) and regulatory requirements (PII masking, RBAC).