Skip to content
cccskills
FreePublished on GitHub

deep-learning-python

Guidelines for deep learning development with PyTorch, Transformers, Diffusers, and Gradio for LLM and diffusion model work.

The English summary is being prepared. Showing the original description.

bouclem/skills
See how to install

Files included (1)

  • SKILL.md2.8 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

Deep Learning Python Development

You are an expert in deep learning, transformers, diffusion models, and LLM development using Python libraries like PyTorch, Diffusers, Transformers, and Gradio. Follow these guidelines when writing deep learning code.

Core Principles

  • Write concise, technical responses with accurate Python examples
  • Prioritize clarity and efficiency in deep learning workflows
  • Use object-oriented programming for architectures; functional programming for data pipelines
  • Implement proper GPU utilization and mixed precision training
  • Follow PEP 8 style guidelines

Deep Learning and Model Development

  • Use PyTorch as primary framework
  • Implement custom nn.Module classes for model architectures
  • Utilize autograd for automatic differentiation
  • Apply proper weight initialization and normalization
  • Select appropriate loss functions and optimization algorithms

Transformers and LLMs

  • Leverage the Transformers library for pre-trained models
  • Correctly implement attention mechanisms and positional encodings
  • Use efficient fine-tuning techniques (LoRA, P-tuning)
  • Handle tokenization and sequences properly

Diffusion Models

  • Employ the Diffusers library for diffusion model work
  • Correctly implement forward/reverse diffusion processes
  • Utilize appropriate noise schedulers and sampling methods
  • Understand different pipelines (StableDiffusionPipeline, StableDiffusionXLPipeline)

Training and Evaluation

  • Implement efficient PyTorch DataLoaders
  • Use proper train/validation/test splits
  • Apply early stopping and learning rate scheduling
  • Use task-appropriate evaluation metrics
  • Implement gradient clipping and NaN/Inf handling

Gradio Integration

  • Create interactive demos for inference and visualization
  • Build user-friendly interfaces with proper error handling

Error Handling

  • Use try-except blocks for error-prone operations
  • Implement proper logging
  • Leverage PyTorch's debugging tools

Performance Optimization

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

Required Dependencies

  • torch
  • transformers
  • diffusers
  • gradio
  • numpy
  • tqdm
  • tensorboard/wandb

Project Conventions

  1. Begin with clear problem definition and dataset analysis
  2. Create modular code with separate files for models, data loading, training, evaluation
  3. Use YAML configuration files for hyperparameters
  4. Implement experiment tracking and model checkpointing
  5. Use version control for code and configuration tracking

Reviews

No reviews yet. Tell others what you thought.

Comments

No login needed. Share how it worked for you, ask a question, or note where it did not run.

No comments yet. Be the first to write one.

Comments are published right away. Do not include personal information such as email addresses or phone numbers. Up to 2 URLs.

0 / 1,000 characters

This form is protected by reCAPTCHA, and the Google Privacy Policy and Terms of Service apply.

More skills from this repository

academic-paper

Free

12-agent academic paper writing pipeline. 10 modes (full/plan/outline/revision/revision-coach/abstract/lit-review/format-convert/citation-check/disclosure). 6 paper types, 5 citation formats, bilingual abstracts, LaTeX/DOCX-via-Pandoc/PDF output. Style Calibration + Writing Quality Check + Anti-Patterns with IRON RULE markers. Triggers: write paper, academic paper, guide my paper, parse reviews, AI disclosure, 寫論文, 學術論文, 引導我寫論文, 審查意見.

The English summary is being prepared. Showing the original description.

bouclem/skills7Updated May 31, 2026

academic-paper-reviewer

Free

Multi-perspective academic paper review with dynamic reviewer personas. Simulates 5 independent reviewers (EIC + 3 peer reviewers + Devil's Advocate) with field-specific expertise. Supports full review, re-review (verification), quick assessment, methodology focus, Socratic guided, and calibration modes. Triggers on: review paper, peer review, manuscript review, referee report, review my paper, critique paper, simulate review, editorial review, calibrate reviewer, reviewer calibration, measure reviewer accuracy.

The English summary is being prepared. Showing the original description.

bouclem/skills7Updated May 31, 2026

academic-pipeline

Free

Orchestrator for the full academic research pipeline: research -> write -> integrity check -> review -> revise -> re-review -> re-revise -> final integrity check -> finalize. Coordinates deep-research, academic-paper, and academic-paper-reviewer into a seamless 10-stage workflow with mandatory integrity verification, two-stage peer review, and reproducible quality gates. Triggers on: academic pipeline, research to paper, full paper workflow, paper pipeline, end-to-end paper, research-to-publication, complete paper workflow.

The English summary is being prepared. Showing the original description.

bouclem/skills7Updated May 31, 2026

ad-creative

Free

Create, iterate, and scale paid ad creative for Google Ads, Meta, LinkedIn, TikTok, and similar platforms. Use when generating headlines, descriptions, primary text, or large sets of ad variations for testing and performance optimization.

The English summary is being prepared. Showing the original description.

bouclem/skills7Updated May 31, 2026

advanced-evaluation

Free

This skill should be used when the user asks to "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise comparison, position bias, evaluation pipelines, or automated quality assessment.

The English summary is being prepared. Showing the original description.

bouclem/skills7Updated May 31, 2026

ai-agent-development

Free

AI agent development workflow for building autonomous agents, multi-agent systems, and agent orchestration with CrewAI, LangGraph, and custom agents.

The English summary is being prepared. Showing the original description.

bouclem/skills7Updated May 31, 2026

See all skills by bouclem

Report a problem with this skill