yoshitomo-matsubara/torchdistill

Health v2 · maintenance only

Adoption not part of the score

1629 stars · 145 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

torchdistill is a modular, configuration-driven PyTorch framework for knowledge distillation and general deep learning experiments, requiring no coding—experiments are defined via declarative YAML files. It implements 26+ state-of-the-art distillation methods and provides trained models, logs, and configs for reproducibility.

Use cases

• distill a large teacher model into a smaller student model
• run knowledge distillation experiments without writing Python code
• reproduce published knowledge distillation results from papers
• extract intermediate representations from a model without changing its forward interface
• train image classification models on ImageNet or CIFAR via YAML config
• perform object detection or semantic segmentation experiments on COCO or PASCAL VOC
• run text classification experiments on GLUE tasks with Hugging Face models

When to choose

• you need to apply or benchmark state-of-the-art knowledge distillation methods in PyTorch
• you want reproducible deep learning experiments driven by declarative YAML configs instead of code
• you need to distill models across vision and NLP tasks with pretrained checkpoints available
• you want to extract intermediate model features via forward hooks without modifying model code

When to avoid

• you need a general-purpose training framework with no distillation focus and prefer writing plain PyTorch
• your project uses a framework other than PyTorch, such as TensorFlow or JAX
• you need highly custom training loops that don't fit the config-driven design
• you want a no-code GUI tool rather than YAML-based configuration

Facets

framework · maturity active

deep-learning machine-learning llm-training nlp image-processing computer-vision configuration-management deep-learning machine-learning computer-vision developer-tools python cross-platform knowledge-distillation pytorch yaml-config model-compression teacher-student reproducibility image-classification object-detection semantic-segmentation text-classification natural-language-processing gpu

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