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Reference implementations of MLPerf® training benchmarks
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Scanned 2026-07-15 03:16
llama2_70b_lora/requirements.txt
python ecosystem,
5 dependencies
llm_moe_pretraining/nemo/requirements.txt
python ecosystem,
10 dependencies
recommendation_v2/torchrec_dlrm/requirements.txt
python ecosystem,
3 dependencies
retired_benchmarks/ncf/requirements.txt
python ecosystem,
7 dependencies
retired_benchmarks/resnet-tf1/requirements.txt
python ecosystem,
4 dependencies
retired_benchmarks/retinanet/requirements.txt
python ecosystem,
5 dependencies
retired_benchmarks/ssd-v1/requirements.txt
python ecosystem,
13 dependencies
retired_benchmarks/stable_diffusion/requirements.txt
python ecosystem,
24 dependencies
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Reference implementations of MLPerf® inference benchmarks
Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024)
Ongoing research training transformer models at scale
Making large AI models cheaper, faster and more accessible
Benchmarking large language models' complex reasoning ability with chain-of-thought prompting
OpenMMLab Foundational Library for Training Deep Learning Models
