Instructions to use Motif-Technologies/optimizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Kernels
How to use Motif-Technologies/optimizer with Kernels:
# !pip install kernels from kernels import get_kernel kernel = get_kernel("Motif-Technologies/optimizer") - Notebooks
- Google Colab
- Kaggle
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tags:
- kernel
---
# Optimizer
Optimizer is a python package that provides:
- PyTorch implementation of recent optimizer algorithms
- with support for parallelism techniques for efficient large-scale training.
### Currently implemented
- [Parallel Muon with FSDP2](./docs/muon/parallel_muon.pdf)
## Usage
```python
import torch
from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
from kernels import get_kernel
optimizer = get_kernel("motif-technologies/optimizer")
model = None # your model here
fsdp_model = FSDP(model)
optim = optimizer.Muon(
fsdp_model.parameters(),
lr=0.01,
momentum=0.9,
weight_decay=1e-4,
)
```
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