A microframework on top of PyTorch with first-class citizen APIs for foundation model adaptation https://refine.rs/
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Cédric Deltheil df0cc2aeb8 do not call __getattr__ with keyword argument
Same for __setattr__. Use positional arguments instead. E.g.:

    import torch
    import refiners.fluxion.layers as fl
    m = torch.compile(fl.Linear(1,1))
    m(torch.zeros(1))
    # TypeError: Module.__getattr__() got an unexpected keyword argument 'name'
2024-03-25 21:46:13 +01:00
.github/workflows fix stalebot message config 2024-03-11 17:05:14 +01:00
assets README: upgrade hello world 2023-10-20 18:28:31 +02:00
docs change TimeValue to a dataclass 2024-03-19 14:49:24 +01:00
notebooks deprecate outdated notebooks/basics.ipynb 2024-02-02 14:12:59 +01:00
scripts Add HQ-SAM Adapter 2024-03-21 15:36:55 +01:00
src/refiners do not call __getattr__ with keyword argument 2024-03-25 21:46:13 +01:00
tests SAM init with mask_decoder after #325 2024-03-24 20:18:57 +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 HQ-SAM logit equal test, following #331 2024-03-23 21:58:32 +01:00
README.md Update README.md with HQ-SAM news 2024-03-25 09:19:19 +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 HQ-SAM Adapter 2024-03-21 15:36:55 +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}}
}