Add paramax

patrick-kidger · Jan 23, 2025 · a7f9748 · a7f9748
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diff --git a/README.md b/README.md
@@ -89,7 +89,8 @@ If you found this library to be useful in academic work, then please cite: ([arX
 **Deep learning**  
 [Optax](https://github.com/deepmind/optax): first-order gradient (SGD, Adam, ...) optimisers.  
 [Orbax](https://github.com/google/orbax): checkpointing (async/multi-host/multi-device).  
-[Levanter](https://github.com/stanford-crfm/levanter): scalable+reliable training of foundation models (e.g. LLMs).  
+[Levanter](https://github.com/stanford-crfm/levanter): scalable+reliable training of foundation models (e.g. LLMs).
+[paramax](https://github.com/danielward27/paramax): parameterizations and constraints for PyTrees.
 
 **Scientific computing**  
 [Diffrax](https://github.com/patrick-kidger/diffrax): numerical differential equation solvers.  

diff --git a/docs/index.md b/docs/index.md
@@ -78,7 +78,8 @@ If this quick start has got you interested, then have a read of [All of Equinox]
 **Deep learning**  
 [Optax](https://github.com/deepmind/optax): first-order gradient (SGD, Adam, ...) optimisers.  
 [Orbax](https://github.com/google/orbax): checkpointing (async/multi-host/multi-device).  
-[Levanter](https://github.com/stanford-crfm/levanter): scalable+reliable training of foundation models (e.g. LLMs).  
+[Levanter](https://github.com/stanford-crfm/levanter): scalable+reliable training of foundation models (e.g. LLMs).
+[paramax](https://github.com/danielward27/paramax): parameterizations and constraints for PyTrees.
 
 **Scientific computing**  
 [Diffrax](https://github.com/patrick-kidger/diffrax): numerical differential equation solvers.