A microframework on top of PyTorch with first-class citizen APIs for foundation model adaptation https://refine.rs/
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Pierre Chapuis f8d55ccb20 add LcmAdapter
This adds support for the condition scale embedding.
Also updates the UNet converter to support LCM.
2024-02-21 16:37:27 +01:00
.github/workflows add spelling.yml to spot spelling mistakes 2024-02-07 17:51:25 +01:00
assets README: upgrade hello world 2023-10-20 18:28:31 +02:00
docs enable StyleAligned related docstrings in mkdocstrings 2024-02-15 15:22:47 +01:00
notebooks deprecate outdated notebooks/basics.ipynb 2024-02-02 14:12:59 +01:00
scripts add LcmAdapter 2024-02-21 16:37:27 +01:00
src/refiners add LcmAdapter 2024-02-21 16:37:27 +01:00
tests add LCMSolver (Latent Consistency Models) 2024-02-21 16:37:27 +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 add typos to dev-dependencies, also remove ruff from non dev-dependencies 2024-02-09 12:12:51 +01:00
README.md add StyleAligned to the README's "Latest News 🔥" 2024-02-15 15:22:47 +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 add typos to dev-dependencies, also remove ruff from non dev-dependencies 2024-02-09 12:12:51 +01:00

Finegrain Refiners Library

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

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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}}
}