Files
tinygrad/examples/mlperf
qazal 5bffa17f82 llama train: better NULL=1 EMULATE=AMD_CDNA4 dev experience (#14395)
* beam opens devices

* switch to hip renderer

* amd: true?

* llvm true is for test_autogen
2026-01-28 17:31:22 +09:00
..
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Each model should be a clean single file.
They are imported from the top level `models` directory

It should be capable of loading weights from the reference imp.

We will focus on these 5 models:

# Resnet50-v1.5 (classic) -- 8.2 GOPS/input
# Retinanet
# 3D UNET (upconvs)
# RNNT
# BERT-large (transformer)

They are used in both the training and inference benchmark:
https://mlcommons.org/en/training-normal-21/
https://mlcommons.org/en/inference-edge-30/
And we will submit to both.

NOTE: we are Edge since we don't have ECC RAM