golololologol/LLM-Distillery

A pipeline for LLM knowledge distillation

117

stars

113

commits

Python

primary language

May 7, 2026

updated

distillation
knowledge-distillation
llm
llm-training

README

LLM-Distillery

LLM-Distillery is a pipeline for distillation of one or multiple teacher models into a student.

Main features:

  • Single and Multi-Teacher distillation
  • Distillation on instruct and completion text
  • Offline distillation: collects the dataset, and only then trains (Yes, you can share the collected datasets)
  • Windows and Linux support
  • Automatic hdf5 dataset synchronization, with continued collection after force-exit
  • Lots of knobs to tweak! From temperature to the device mapping strategy
  • And a lot more!

Installation

See Wiki for installation instructions

Console UI

https://github.com/user-attachments/assets/baac01ab-a045-44ec-a752-6662e9304a60

(Full run of tinyllama 1.1B self-distillation from full fp16 model to 4bit quantized version)

Contributions

Big thanks to kalomaze for help and keeping me sane while I was building this project!
Also, thanks to AlpinDale for giving access to compute during the development!

If you want to contribute to this project, feel free!
Open issues when you encounter them, and make PRs when you feel like it.

Contributors

golololologol

113 commits

golololologol/LLM-Distillery

A pipeline for LLM knowledge distillation

117

stars

113

commits

Python

primary language

May 7, 2026

updated

distillation
knowledge-distillation
llm
llm-training

README

LLM-Distillery

LLM-Distillery is a pipeline for distillation of one or multiple teacher models into a student.

Main features:

  • Single and Multi-Teacher distillation
  • Distillation on instruct and completion text
  • Offline distillation: collects the dataset, and only then trains (Yes, you can share the collected datasets)
  • Windows and Linux support
  • Automatic hdf5 dataset synchronization, with continued collection after force-exit
  • Lots of knobs to tweak! From temperature to the device mapping strategy
  • And a lot more!

Installation

See Wiki for installation instructions

Console UI

https://github.com/user-attachments/assets/baac01ab-a045-44ec-a752-6662e9304a60

(Full run of tinyllama 1.1B self-distillation from full fp16 model to 4bit quantized version)

Contributions

Big thanks to kalomaze for help and keeping me sane while I was building this project!
Also, thanks to AlpinDale for giving access to compute during the development!

If you want to contribute to this project, feel free!
Open issues when you encounter them, and make PRs when you feel like it.

Contributors

golololologol

113 commits

Languages

Python

99.8%