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GRPO RL Training

grpo-rl-training

Orchestra-Research/AI-Research-SKILLs

Provides expert guidance for implementing GRPO with TRL to fine-tune reasoning models, enforce structured outputs, and optimize custom reward functions for verifiable and multi-objective tasks.

Accelerate Distributed Training

huggingface-accelerate

Orchestra-Research/AI-Research-SKILLs

Accelerate wraps PyTorch scripts with a four-line integration so you can launch DDP, DeepSpeed, FSDP, Megatron, or mixed-precision training on any hardware via a single command, handling device placement, sharding, and automatic config generation.

Hyperparameter Tuner Assistant

hyperparameter-tuner

jeremylongshore/claude-code-plugins-plus-skills

Automated guidance for hyperparameter tuning within ML training workflows, offering best practices, production-ready code, and validation across modeling stages like data prep, training, and experiment tracking.

LitGPT LLM Workflows

implementing-llms-litgpt

Orchestra-Research/AI-Research-SKILLs

LitGPT delivers single-file LLM implementations and workflows over 20+ pretrained architectures, making it easy to explore, fine-tune with LoRA/QLoRA, or run production-ready training via Lightning AI.

Lambda Labs GPU Cloud

lambda-labs-gpu-cloud

Orchestra-Research/AI-Research-SKILLs

Lambda Labs GPU cloud offers reserved and on-demand instances with SSH access, persistent filesystems, and 1-Click multi-node clusters, making it ideal for long-running training and inference workloads that need high-performance GPUs.

Learning Rate Scheduler

learning-rate-scheduler

jeremylongshore/claude-code-plugins-plus-skills

Provides automated guidance for configuring learning rate schedulers during ML training, covering best practices, setup patterns, and framework-ready code snippets for PyTorch, TensorFlow, and Scikit-learn while validating against common standards.

Enterprise Miles RL

miles-rl-training

Orchestra-Research/AI-Research-SKILLs

Guidance for training large multimodal MoE models with miles, covering FP8/INT4 quantization-aware regimes, train-inference alignment, speculative RL throughput tricks, and enterprise stability practices for production deployments.

Mixed Precision Trainer

mixed-precision-trainer

jeremylongshore/claude-code-plugins-plus-skills

Automates mixed precision ML training workflows by surfacing best practices, generating runnable code/configurations, and validating setups across data prep, model tuning, and experiment tracking.

Production ML Pipeline

Jeffallan/claude-skills

Designs production-grade ML pipeline infrastructure covering experiment tracking, feature stores, DAG orchestration, and automated retraining so teams can reliably version data, tune hyperparameters, and deploy models with tools like MLflow, Kubeflow, Airflow, or Feast.

ML Pipeline Workflow

ml-pipeline-workflow

sickn33/antigravity-awesome-skills

Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating mod...

Mlflow Tracking Setup

mlflow-tracking-setup

jeremylongshore/claude-code-plugins-plus-skills

Automates guidance for configuring mlflow experiment tracking, covering best practices, code generation, and validation to support ML training workflows.

Modal Serverless GPU

modal-serverless-gpu

Orchestra-Research/AI-Research-SKILLs

Modal's serverless GPU cloud platform lets teams run ML training, inference, and batch jobs with pay-per-second pricing, automatic scaling, Python-native infra definitions, fast cold starts, and container caching.

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