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Fine-Tune Transformer Model

Fine-tune transformer language models using TRL with support for SFT, DPO, GRPO, and reward model training.

8 skills · pack

Results for “training”

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agentskillexchange

weights-biases-run-monitor

Streams live training metrics, system stats, and gradients from active W&B runs, alerts on metric regressions, and posts summaries to Slack.

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Cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Enables building and training quantum circuits with automatic differentiation, seamless integration with PyTorch/JAX/TensorFlow, and device-independent execution across simulators and quantum hardware (IBM, Amazon Braket, Google, Rigetti, IonQ, etc.). Use when working with quantum circuits, variational quantum algorithms (VQE, QAOA), quantum neural networks, hybrid quantum-classical models, molecular simulations, quantum chemistry calculations, or any quantum computing tasks requiring gradient-based optimization, hardware-agnostic programming, or quantum machine learning workflows.