docs: document Candlex in Nx README (#1368)

nx/README.md

@@ -12,7 +12,7 @@ Nx is a multi-dimensional tensors library for Elixir with multi-staged compilati
 
   * Auto-vectorization and auto-batching, which allow developers to elegantly transform numerical code that only work on specific dimensions, into code that efficiently runs on higher dimensions in parallel
 
-  * Numerical definitions, known as `defn`, is a subset of Elixir that is compilable to multiple targets, including GPUs. See [EXLA](https://github.com/elixir-nx/nx/tree/main/exla) for just-in-time (JIT) compilation for CPUs/GPUs/TPUs and [Torchx](https://github.com/elixir-nx/nx/tree/main/torchx) for CPUs/GPUs support;
+  * Numerical definitions, known as `defn`, is a subset of Elixir that is compilable to multiple targets, including GPUs. See [EXLA](https://github.com/elixir-nx/nx/tree/main/exla) for just-in-time (JIT) compilation for CPUs/GPUs/TPUs and [Torchx](https://github.com/elixir-nx/nx/tree/main/torchx) or [Candlex](https://github.com/mimiquate/candlex) for CPUs/GPUs support;
 
   * Built-in distributed² serving: encapsulate complex numerical pipelines into `Nx.Serving`. Servings provide batching, streaming, and partitioning out of the box. You can distribute servings over multiple CPU cores and GPU devices, as well as over a cluster of machines;
 
@@ -97,7 +97,7 @@ iex> Nx.divide(Nx.exp(t), Nx.sum(Nx.exp(t)))
 >
 ```
 
-By default, `Nx` uses pure Elixir code. Since Elixir is a functional and immutable language, each operation above makes a copy of the tensor, which is quite innefficient. You can use either [EXLA](https://github.com/elixir-nx/nx/tree/main/exla) or [Torchx](https://github.com/elixir-nx/nx/tree/main/torchx) backends for an improvement in performance, often over 3 orders of magnitude, as well as the ability to work on the data in the GPU. See the README of those projects for more information.
+By default, `Nx` uses pure Elixir code. Since Elixir is a functional and immutable language, each operation above makes a copy of the tensor, which is quite innefficient. You can use either [EXLA](https://github.com/elixir-nx/nx/tree/main/exla), [Torchx](https://github.com/elixir-nx/nx/tree/main/torchx) or [Candlex](https://github.com/mimiquate/candlex) backends for an improvement in performance, often over 3 orders of magnitude, as well as the ability to work on the data in the GPU. See the README of those projects for more information.
 
 ## Numerical definitions