diff --git a/colibri/doc/sphinx/source/available-models/linear-model.rst b/colibri/doc/sphinx/source/available-models/linear-model.rst
index aa97dced..978bbd8b 100644
--- a/colibri/doc/sphinx/source/available-models/linear-model.rst
+++ b/colibri/doc/sphinx/source/available-models/linear-model.rst
@@ -1,8 +1,8 @@
 .. _linear-model:
 
-============
+############
 Linear Model
-============
+############
 
 This model was presented in Ref. :cite:alp:`Costantini:2025wxp`.
 
@@ -14,15 +14,34 @@ What is this model for?
 This model is especially suitable for :ref:`running bayesian fits <in_running_bayesian>`.
 It can be used to:
 
-1. **Bayesian PDF Fits with POD Parametrisation** 
-2. **POD Basis Construction** Generate a Proper Orthogonal Decomposition (POD) basis (see Ref. :cite:alp:`Costantini:2025wxp` for details on what this is). 
+1. :ref:`Construct a Proper Orthogonal Decomposition (POD) basis <pod-basis>` (see Ref. :cite:alp:`Costantini:2025wxp` for details on what this is). 
+2. :ref:`Run Bayesian PDF Fits with the POD Parametrisation <pod-fits>`.
 
 
+Model description
+-----------------
+
+This model parametrises PDFs as linear combinations of basis functions:
+
+.. math::
+
+    f_w(x) = \phi_0(x) + \sum_{k=1}^{N} w_k \phi_k(x),
+
+where :math:`w = (w_1, ..., w_N)` are the parameters to be inferred,
+and :math:`\phi_k(x)` are carefully chosen basis functions, which in
+practice are constructed by applying Proper Orthogonal Decomposition
+(POD) to a basis set of samples of the randomly initialised n3fit Neural
+Network.
+
+For details on the motivation behind this choice of model see Ref. Ref. :cite:alp:`Costantini:2025wxp`.
+
 How to use this model
 ---------------------
 
 You can find installation instructions in the `model repository <https://github.com/HEP-PBSP/wmin-model>`_. 
 
+.. _pod-basis:
+
 Constructing a POD basis
 ^^^^^^^^^^^^^^^^^^^^^^^^
 
@@ -84,5 +103,31 @@ shifted by running:
 where the ``shift_lhapdf_members.py`` script can be found in the directory ``wmin-model/wmin/runcards``
 and ``evolved_directory`` is the fit or POD basis directory that should have previously been evolved.
 
-You can then follow Colibri's :ref:`analytic <running_analytic>` and :ref:`bayesian <in_running_bayesian>`
-workflows to run fits.
\ No newline at end of file
+
+.. _pod-fits:
+
+Running fits
+^^^^^^^^^^^^
+
+You can follow Colibri's :ref:`analytic <running_analytic>` and :ref:`bayesian <in_running_bayesian>`
+workflows to run fits with this model. There are, however, a few points to note that are specific to
+this model.
+
+:underline:`Analytic fits`
+
+Analytic fits are only appropriate for linear models that also have a linear relationship with the
+data, so should be run with DIS data only.
+
+``wmin_settings``
+=================
+
+This model has specific settings that need to be specified in the runcard in order to run a fit:
+
+.. code-block::
+
+    wmin_settings:
+        wminpdfset: 250503_pod_basis_40k 
+        n_basis: 10     # number of parameters/weights to be fitted
+
+* ``wminpdfset`` is the POD basis set you should have constructed before running a fit.
+* ``n_basis`` is the number of parameters or *weights* to be fitted (minimised). It should be less than or equal to the number of replicas in ``wminpdfset``.
\ No newline at end of file
diff --git a/colibri/doc/sphinx/source/get-started/installation.rst b/colibri/doc/sphinx/source/get-started/installation.rst
index 66007e10..0bd97df8 100644
--- a/colibri/doc/sphinx/source/get-started/installation.rst
+++ b/colibri/doc/sphinx/source/get-started/installation.rst
@@ -77,7 +77,7 @@ To verify that the installation went through, run:
 3. GPU (CUDA) JAX Support
 -------------------------
 
-The installation instructions shown above will install jax in *cpu* mode. It is also
+The installation instructions shown above will install jax in CPU mode. It is also
 possible, however, to run Colibri fits using *gpu cuda support*. You will first have
 to install the package following one of the methods described above. Then, if you
 are on a linux machine, you can install jax in cuda mode by running:
diff --git a/colibri/doc/sphinx/source/tutorials/running_fits/running_mc_replica.rst b/colibri/doc/sphinx/source/tutorials/running_fits/running_mc_replica.rst
index 17346dd0..fdf43c18 100644
--- a/colibri/doc/sphinx/source/tutorials/running_fits/running_mc_replica.rst
+++ b/colibri/doc/sphinx/source/tutorials/running_fits/running_mc_replica.rst
@@ -136,9 +136,9 @@ executable.
 ^^^^^^^^^^^^^^^^^^^^^^
 These settings control the method of gradient descent. You can use any
 of the Optax optimizers and settings, which you can read more about
-`here <https://optax.readthedocs.io/en/latest/api/optimizers.html#>`_.
+`here <https://optax.readthedocs.io/en/latest/api/optimizers.html#>`__.
 Learning schedulers are also supported, and you can find the available options
-`here <https://optax.readthedocs.io/en/latest/api/optimizer_schedules.html#>`_.
+`here <https://optax.readthedocs.io/en/latest/api/optimizer_schedules.html#>`__.
 
 ``param_initialiser_settings``
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^