A microframework on top of PyTorch with first-class citizen APIs for foundation model adaptation https://refine.rs/
Find a file
2024-02-02 17:31:14 +01:00
.github/workflows docs.yml: fix changes from #230 2024-02-01 15:57:29 +01:00
assets README: upgrade hello world 2023-10-20 18:28:31 +02:00
configs refactor Lora LoraAdapter and the latent_diffusion/lora file 2024-01-18 16:27:38 +01:00
docs (docs) ignore CLIP related docstrings in latent_diffusion.md 2024-02-02 17:31:14 +01:00
notebooks deprecate outdated notebooks/basics.ipynb 2024-02-02 14:12:59 +01:00
scripts make Scheduler a fl.Module + Change name Scheduler -> Solver 2024-01-31 17:03:52 +01:00
src/refiners (doc/fluxion/ld) add LatentDiffusionAutoencoder docstrings 2024-02-02 17:31:14 +01:00
tests clip text, lda encode batch inputs 2024-02-01 17:05:28 +01:00
.gitignore add rye scripts for code coverage 2024-01-29 15:10:06 +01:00
CONTRIBUTING.md update getting started 2024-02-01 17:17:22 +01:00
LICENSE Update LICENSE 2024-02-02 14:08:09 +01:00
mkdocs.yml docs: wording tweaks (homepage and banner) 2024-02-02 12:24:46 +01:00
pyproject.toml ruff 0.2.0 2024-02-02 14:46:37 +01:00
README.md brush up main README 2024-02-02 16:31:02 +01:00
requirements.docs.txt (pyproject.toml) move doc deps inside their own project.optional-dependencies 2024-02-02 11:08:21 +01:00
requirements.lock ruff 0.2.0 2024-02-02 14:46:37 +01:00

Finegrain Refiners Library

The simplest way to train and run adapters on top of foundation models

Manifesto | Documentation | Guides | Discord


PyPI - Python Version PyPI Status license code bounties chat

Latest News 🔥

Installation

The current recommended way to install Refiners is from source using Rye:

git clone "git@github.com:finegrain-ai/refiners.git"
cd refiners
rye sync --all-features

Documentation

Refiners comes with a MkDocs-based documentation website available at https://refine.rs. You will find there a quick start guide, a description of the key concepts, as well as in-depth foundation model adaptation guides.

Awesome Adaptation Papers

If you're interested in understanding the diversity of use cases for foundation model adaptation (potentially beyond the specific adapters supported by Refiners), we suggest you take a look at these outstanding papers:

Projects using Refiners

Credits

We took inspiration from these great projects:

Citation

@misc{the-finegrain-team-2023-refiners,
  author = {Benjamin Trom and Pierre Chapuis and Cédric Deltheil},
  title = {Refiners: The simplest way to train and run adapters on top of foundation models},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/finegrain-ai/refiners}}
}