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Search PyTorch , found 48 results

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Transfer Learning Adaptation Skill

adapting-transfer-learning-models

jeremylongshore/claude-code-plugins-plus-skills

Automates adapting pretrained models via transfer learning by analyzing requirements, generating PyTorch or TensorFlow fine-tuning code, validating data, tracking KPIs, and saving artifacts so you can quickly reuse existing models for new datasets and tasks.

AudioCraft Music Generation

audiocraft-audio-generation

Orchestra-Research/AI-Research-SKILLs

AudioCraft is Meta’s PyTorch toolkit for text-to-music and text-to-sound creation via MusicGen, AudioGen and EnCodec, making it ideal for prototyping music apps, effects, and controllable stereo pieces.

AudioCraft Music Generation

audiocraft-audio-generation

Orchestra-Research/AI-Research-SKILLs

AudioCraft is Meta’s PyTorch toolkit for text-to-music and text-to-sound creation via MusicGen, AudioGen and EnCodec, making it ideal for prototyping music apps, effects, and controllable stereo pieces.

TorchTitan Distributed Pretraining

distributed-llm-pretraining-torchtitan

Orchestra-Research/AI-Research-SKILLs

Orchestrates PyTorch-native TorchTitan 4D parallelism (FSDP2, TP, PP, CP) for large-scale LLM pretraining, scaling from 8 to 512+ GPUs with Float8, torch.compile, and distributed checkpointing to boot Llama 3.1, DeepSeek V3, or custom models.

Early Stopping Callback

early-stopping-callback

jeremylongshore/claude-code-plugins-plus-skills

Automated guidance for configuring and applying early stopping callbacks across ML training workflows, covering best practices, code generation, and validation for PyTorch, TensorFlow, and scikit-learn experiments.

Gradient Clipping Helper

gradient-clipping-helper

jeremylongshore/claude-code-plugins-plus-skills

Gradient Clipping Helper automates ML training work by guiding gradient clipping workflows, generating production-ready code and configs, and validating outputs for PyTorch, TensorFlow, and related pipelines.

Simplified Distributed Training

huggingface-accelerate

Orchestra-Research/AI-Research-SKILLs

HuggingFace Accelerate adds distributed support to any PyTorch script with just four lines, providing a unified API for DDP, DeepSpeed, FSDP, and Megatron, plus automatic device placement, mixed precision, and simple launch/config commands for fast prototyping.

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.

Production ML Engineering and MLOps

sickn33/antigravity-awesome-skills

A comprehensive skill set for building, deploying, and managing production-grade Machine Learning systems. Expertise spans modern frameworks (PyTorch, TensorFlow), model serving using Kubernetes, advanced feature engineering (Feast, Tecton), and robust MLOps practices (monitoring, A/B testing, CI/CD). Focuses on delivering scalable, reliable, and business-value-driven AI solutions.

MLflow Lifecycle Platform

Orchestra-Research/AI-Research-SKILLs

Track experiments, catalog models, compare versions, and deploy trained artifacts with MLflow’s framework-agnostic lifecycle tools across PyTorch, TensorFlow, and other stacks.

Remote Model Interpretability

nnsight-remote-interpretability

Orchestra-Research/AI-Research-SKILLs

Guides researchers through nnsight’s tracing and activation APIs so you can inspect or modify PyTorch internals locally or via NDIF-powered remote runs on 70B+ foundation models without local GPUs.

Flash Attention Optimizer

optimizing-attention-flash

Orchestra-Research/AI-Research-SKILLs

Flash Attention optimizer delivers 2-4x speedup and 10-20x memory savings for transformer attention, ideal when training or running long-context models that hit GPU memory or latency limits; supports PyTorch native scaled_dot_product_attention, the flash-attn library, H100 FP8 precision, and sliding-window attention.

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