relora
Official code for ReLoRA from the paper Stack More Layers Differently: High-Rank Training Through Low-Rank Updates
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Download Latest Version (.zip)- launch.json
- llama_100m.json
- llama_130m.json
- llama_1b.json
- llama_20m.json
- llama_250m.json
- llama_250m_50K.json
- llama_250m_old.json
- llama_350m.json
- llama_35m.json
- llama_3b.json
- llama_40m.json
- llama_60m.json
- llama_71m.json
- llama_7b.json
- llama_9m.json
- pile_megatron_dataset.yaml
- pythia_tokenizer.json
- 01_peft_pretraining.ipynb
- 02_quick_debugs.ipynb
- 03_scaling_laws_plotting.ipynb
- 04_plot_lr.ipynb
- 05_check_ranks.ipynb
- 06_svd.ipynb
- 07_plotting.ipynb
- 08_ranks_before_and_after.ipynb
- 09_bar_plot.ipynb
- 10_chunking.ipynb
- 11_test_pythia.ipynb
- 12_test_relora_init.ipynb
- 13_zero_optimizer_resets.ipynb
- 14_check_pretokenization.ipynb
- 15_debug_dataloading.ipynb
- 16_quantized.ipynb
- arguments.py
- blendable_dataset.py
- data_utils.py
- dataloader.py
- dataset.py
- helpers.cpp
- indexed_dataset.py
- Makefile
- neox_args.py
- samplers.py
- args_utils.py
- dataloader.py
- modeling_llama.py
- modeling_pythia.py
- relora.py
- training_utils.py
- 1B_v1.0.yaml
- .gitignore
- CITATION.cff
- LICENSE
- pretokenize.py
- README.dev.md
- README.md
- requirements.txt
- run_glue.py
- setup.py
- torchrun_main.py
π Installation Guide
git clone https://github.com/Guitaricet/relora
Downloads the entire project code from GitHub to your computer.
cd relora
Moves into the project folder you just downloaded.
2. Official Install Script
Easy Recommended- Python 3 Python is required to use pip.
pip install flash-attn
Installs the package published on PyPI directly β no need to clone the source.
Pulled directly from this repo's README.
3. Python
Easypip install -e .
Installs the Python libraries listed in requirements.txt (or similar).
pip install flash-attn
Installs the package published on PyPI directly β no need to clone the source.
Pulled directly from this repo's README.
4. Make
Medium- Git Needed to download the project code from GitHub.
- Make Usually pre-installed on Linux/macOS. On Windows, install separately (e.g. via MSYS2 or WSL).
cd peft_pretraining/megatron_dataset
This project's files live in a subfolder, so move into it first.
make
Compiles the code based on the generated build configuration to produce an executable.
