document Levanter

patrick-kidger · Jul 12, 2023 · 2638a35 · 2638a35
1 parent f4d95c5
commit 2638a35
Show file tree

Hide file tree

Showing 2 changed files with 4 additions and 0 deletions.
diff --git a/README.md b/README.md
@@ -72,3 +72,5 @@ If you found this library useful in academic research, please cite: [(arXiv link
 [Eqxvision](https://github.com/paganpasta/eqxvision): computer vision models.
 
 [sympy2jax](https://github.com/google/sympy2jax): SymPy<->JAX conversion; train symbolic expressions via gradient descent.
+
+[Levanter](https://github.com/stanford-crfm/levanter): scalable+reliable training of foundation models (e.g. LLMs).
diff --git a/docs/index.md b/docs/index.md
@@ -60,3 +60,5 @@ Have a look at the [Getting Started](./usage/getting-started.md) page.
 [Eqxvision](https://github.com/paganpasta/eqxvision): computer vision models.
 
 [sympy2jax](https://github.com/google/sympy2jax): SymPy<->JAX conversion; train symbolic expressions via gradient descent.
+
+[Levanter](https://github.com/stanford-crfm/levanter): scalable+reliable training of foundation models (e.g. LLMs).
Original file line number	Diff line number	Diff line change
Expand Up		@@ -72,3 +72,5 @@ If you found this library useful in academic research, please cite: [(arXiv link
		[Eqxvision](https://github.com/paganpasta/eqxvision): computer vision models.

		[sympy2jax](https://github.com/google/sympy2jax): SymPy<->JAX conversion; train symbolic expressions via gradient descent.

		[Levanter](https://github.com/stanford-crfm/levanter): scalable+reliable training of foundation models (e.g. LLMs).
Original file line number	Diff line number	Diff line change
Expand Up		@@ -60,3 +60,5 @@ Have a look at the [Getting Started](./usage/getting-started.md) page.
		[Eqxvision](https://github.com/paganpasta/eqxvision): computer vision models.

		[sympy2jax](https://github.com/google/sympy2jax): SymPy<->JAX conversion; train symbolic expressions via gradient descent.

		[Levanter](https://github.com/stanford-crfm/levanter): scalable+reliable training of foundation models (e.g. LLMs).