docs update

docs/build/html/_sources/dev/extensions.rst

@@ -486,9 +486,8 @@ below.
         std::ostringstream kname;
         kname << "axpby_" << "general_" << type_to_name(out);
 
-        // Make sure the metal library is available and look for it
-        // in the same folder as this executable if needed
-        d.register_library("mlx_ext", metal::get_colocated_mtllib_path);
+        // Make sure the metal library is available
+        d.register_library("mlx_ext");
 
         // Make a kernel from this metal library
         auto kernel = d.get_kernel(kname.str(), "mlx_ext");

docs/build/html/_sources/examples/llama-inference.rst

@@ -15,7 +15,7 @@ module to concisely define the model architecture.
 Attention layer
 ^^^^^^^^^^^^^^^^
 
-We will start with the llama attention layer which notably uses the RoPE
+We will start with the Llama attention layer which notably uses the RoPE
 positional encoding. [1]_ In addition, our attention layer will optionally use a
 key/value cache that will be concatenated with the provided keys and values to
 support efficient inference.

docs/build/html/_sources/examples/mlp.rst

@@ -64,7 +64,7 @@ set:
 Next, setup the problem parameters and load the data. To load the data, you need our
 `mnist data loader
 <https://github.com/ml-explore/mlx-examples/blob/main/mnist/mnist.py>`_, which
-we will import as `mnist`.
+we will import as ``mnist``.
 
 .. code-block:: python
 

docs/build/html/_sources/install.rst

@@ -70,36 +70,36 @@ To build and install the MLX python library from source, first, clone MLX from
 
    git clone git@github.com:ml-explore/mlx.git mlx && cd mlx
 
-Install `nanobind <https://nanobind.readthedocs.io/en/latest/>`_ with:
-
-.. code-block:: shell
-
-    pip install git+https://github.com/wjakob/nanobind.git@2f04eac452a6d9142dedb957701bdb20125561e4
-
 Then simply build and install MLX using pip:
 
 .. code-block:: shell
 
-   env CMAKE_BUILD_PARALLEL_LEVEL="" pip install .
+  CMAKE_BUILD_PARALLEL_LEVEL="" pip install .
 
-For developing use an editable install:
+For developing, install the package with development dependencies, and use an
+editable install:
 
 .. code-block:: shell
 
-  env CMAKE_BUILD_PARALLEL_LEVEL="" pip install -e .
+  CMAKE_BUILD_PARALLEL_LEVEL="" pip install -e ".[dev]"
 
-To make sure the install is working run the tests with:
+Once the development dependencies are installed, you can build faster with:
+
+.. code-block:: shell
+
+ CMAKE_BUILD_PARALLEL_LEVEL="" python setup.py build_ext -j --inplace
+
+Run the tests with:
 
 .. code-block:: shell
 
-  pip install ".[testing]"
   python -m unittest discover python/tests
 
-Optional: Install stubs to enable auto completions and type checking from your IDE:
+Optional: Install stubs to enable auto completions and type checking from your
+IDE:
 
 .. code-block:: shell
 
-  pip install ".[dev]"
   python setup.py generate_stubs
 
 C++ API

docs/build/html/_sources/python/_autosummary/mlx.core.DtypeCategory.rst

@@ -9,14 +9,21 @@
    .. automethod:: __init__
 
    
-   .. rubric:: Methods
+   
+
+   
+   
+   .. rubric:: Attributes
 
    .. autosummary::
    
-      ~DtypeCategory.__init__
-   
-   
-
-   
+      ~DtypeCategory.complexfloating
+      ~DtypeCategory.floating
+      ~DtypeCategory.inexact
+      ~DtypeCategory.signedinteger
+      ~DtypeCategory.unsignedinteger
+      ~DtypeCategory.integer
+      ~DtypeCategory.number
+      ~DtypeCategory.generic
    
    

docs/build/html/_sources/python/_autosummary/mlx.core.linalg.cholesky_inv.rst

@@ -0,0 +1,6 @@
+mlx.core.linalg.cholesky\_inv
+=============================
+
+.. currentmodule:: mlx.core.linalg
+
+.. autofunction:: cholesky_inv

docs/build/html/_sources/python/_autosummary/mlx.core.linalg.tri_inv.rst

@@ -0,0 +1,6 @@
+mlx.core.linalg.tri\_inv
+========================
+
+.. currentmodule:: mlx.core.linalg
+
+.. autofunction:: tri_inv

docs/build/html/_sources/python/linalg.rst

@@ -9,7 +9,9 @@ Linear Algebra
    :toctree: _autosummary 
 
     inv
+    tri_inv
     norm
     cholesky
+    cholesky_inv
     qr
     svd