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31 changes: 31 additions & 0 deletions easybuild/easyconfigs/p/PEFT/PEFT-0.17.1-foss-2024a-CUDA-12.6.0.eb
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easyblock = 'PythonBundle'

name = 'PEFT'
version = '0.17.1'
versionsuffix = '-CUDA-%(cudaver)s'

homepage = 'https://huggingface.co/PEFT'
description = """
PEFT is integrated with Transformers for easy model training and inference,
Diffusers for conveniently managing different adapters, and Accelerate for
distributed training and inference for really big models.
"""
docurls = ['https://huggingface.co/docs/peft']

toolchain = {'name': 'foss', 'version': '2024a'}

dependencies = [
('CUDA', '12.6.0', '', SYSTEM),
('Python', '3.12.3'),
('SciPy-bundle', '2024.05'),
('Transformers', '4.55.0'),
('accelerate', '1.10.0', versionsuffix),
]

exts_list = [
('peft', version, {
'checksums': ['e6002b42517976c290b3b8bbb9829a33dd5d470676b2dec7cb4df8501b77eb9f'],
}),
]

moduleclass = 'ai'
33 changes: 33 additions & 0 deletions easybuild/easyconfigs/t/TRL/TRL-0.22.0-foss-2024a-CUDA-12.6.0.eb
Original file line number Diff line number Diff line change
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easyblock = 'PythonBundle'

name = 'TRL'
version = '0.22.0'
versionsuffix = '-CUDA-%(cudaver)s'

homepage = 'https://github.com/huggingface/trl'
description = """TRL is a cutting-edge library designed for post-training foundation models
using advanced techniques like Supervised Fine-Tuning (SFT), Proximal Policy
Optimization (PPO), and Direct Preference Optimization (DPO). Built on top of
the HuggingFace Transformers ecosystem, TRL supports a variety of model
architectures and modalities, and can be scaled-up across various hardware
setups."""
docurls = ['https://huggingface.co/docs/peft']

toolchain = {'name': 'foss', 'version': '2024a'}

dependencies = [
('CUDA', '12.6.0', '', SYSTEM),
('Python', '3.12.3'),
('SciPy-bundle', '2024.05'),
('Transformers', '4.55.0'),
('accelerate', '1.10.0', versionsuffix),
('HF-Datasets', '4.0.0'),
]

exts_list = [
('trl', version, {
'checksums': ['7b9ceeff9580c47b926b13fd636fb5e99173496b19f872f747b5aa6095d440e8'],
}),
]

moduleclass = 'ai'
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