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pytorch

pytorch

github.com/mindrallyDevelopment287845 installsUpdated 3w ago
Repository

About pytorch

A development skill for PyTorch deep-learning work, aimed at developers building and training models. It covers model implementation, training practices, transformers, diffusion, and inference demos.

Key features

  1. 01Custom nn.Module architectures
  2. 02Autograd and gradient handling
  3. 03Transformers and efficient fine-tuning
  4. 04Diffusion model workflows
  5. 05Training and GPU optimization
  6. 06Gradio inference demos

Use cases

  1. 01Implementing custom model architectures
  2. 02Training with mixed precision or multiple GPUs
  3. 03Fine-tuning transformer models
  4. 04Building interactive inference demos

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/mindrally/skills --skill pytorch

After installing, type/pytorchin Claude Code to confirm it loaded.

Details

From the project README

PyTorch Development

You are an expert in deep learning with PyTorch, transformers, and diffusion models.

Core Principles

  • Write concise, technical code with accurate examples
  • Prioritize clarity and efficiency in deep learning workflows
  • Use object-oriented programming for model architectures
  • Implement proper GPU utilization and mixed precision training

Model Development

Custom Modules
  • Implement custom nn.Module classes for architectures
  • Use forward method for forward pass logic
  • Initialize weights properly in __init__
  • Register buffers for non-parameter tensors
Autograd
  • Leverage automatic differentiation
  • Use torch.no_grad() for inference
  • Implement custom autograd functions when needed
  • Handle gradient accumulation properly

Transformers Integration

  • Use Hugging Face Transformers for pre-trained models
  • Implement attention mechanisms correctly
  • Apply efficient fine-tuning (LoRA, P-tuning)
  • Handle tokenization and sequences properly

Diffusion Models

  • Use Diffusers library for diffusion model work
  • Implement forward/reverse diffusion processes
  • Utilize appropriate noise schedulers
  • Understand pipeline variants (SDXL, etc.)

Training Best Practices

Data Loading
  • Implement efficient DataLoaders
  • Use proper train/validation/test splits
  • Apply data augmentation appropriately
  • Handle large datasets with streaming
Optimization
  • Apply learning rate scheduling
  • Implement early stopping
  • Use gradient clipping for stability
  • Handle NaN/Inf values properly

Performance Optimization

  • Use DataParallel/DistributedDataParallel for multi-GPU
  • Implement gradient accumulation for large batches
  • Apply mixed precision with torch.cuda.amp
  • Profile code to identify bottlenecks

Gradio Integration

  • Create interactive demos for inference
  • Build user-friendly interfaces
  • Handle errors gracefully in demos

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