A microframework on top of PyTorch with first-class citizen APIs for foundation model adaptation https://refine.rs/
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Pierre Chapuis 8c7fcbc00f LoRA manager: move exclude / include to add_loras call
Always exclude the TimestepEncoder by default.
This is because some keys include both e.g. `resnet` and `time_emb_proj`.

Preprocess blocks that tend to mix up with others in a separate
auto_attach call.
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.github/workflows update deps and use ruff in Rye to format 2024-03-05 19:40:52 +01:00
assets README: upgrade hello world 2023-10-20 18:28:31 +02:00
docs remove add_multiple_loras 2024-03-08 15:43:57 +01:00
notebooks deprecate outdated notebooks/basics.ipynb 2024-02-02 14:12:59 +01:00
scripts add sanity check to auto_attach_loras 2024-03-08 15:43:57 +01:00
src/refiners LoRA manager: move exclude / include to add_loras call 2024-03-08 15:43:57 +01:00
tests LoRA manager: move exclude / include to add_loras call 2024-03-08 15:43:57 +01:00
.gitignore add rye scripts for code coverage 2024-01-29 15:10:06 +01:00
CONTRIBUTING.md update deps and use ruff in Rye to format 2024-03-05 19:40:52 +01:00
LICENSE Update LICENSE 2024-02-02 14:08:09 +01:00
mkdocs.yml write Training 101 guide 2024-02-26 14:44:02 +01:00
pyproject.toml update deps and use ruff in Rye to format 2024-03-05 19:40:52 +01:00
README.md update README 2024-02-26 12:14:02 +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 update deps and use ruff in Rye to format 2024-03-05 19:40:52 +01:00

Finegrain Refiners Library

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

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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}}
}