diff --git a/cosmopower/wrappers/cobaya/cosmopower.py b/cosmopower/wrappers/cobaya/cosmopower.py
index 9792b26..645af7a 100644
--- a/cosmopower/wrappers/cobaya/cosmopower.py
+++ b/cosmopower/wrappers/cobaya/cosmopower.py
@@ -33,6 +33,11 @@ def initialize(self) -> None:
         self._networks = self.parser.restore_networks()
         self._networks_to_eval = []
 
+        self._params_required_by_networks = {}
+
+        for nw in self._networks:
+            self._params_required_by_networks.update({str(p) : None for p in self._networks[nw].parameters})
+
     def must_provide(self, **requirements: dict) -> dict:
         super().must_provide(**requirements)
 
@@ -277,6 +282,11 @@ def get_Cl(self, ell_factor: bool = False,
 
         return cls
 
+
+    def get_requirements(self):
+
+        return self._params_required_by_networks
+
     def get_in_dict(self, dct: dict, path: str) -> Any:
         """
         Networks are labeled with paths like "Cl/tt". This function parses
diff --git a/demos/camb_highacc.yaml b/demos/camb_highacc.yaml
deleted file mode 100644
index 0999e03..0000000
--- a/demos/camb_highacc.yaml
+++ /dev/null
@@ -1,138 +0,0 @@
-network_name: camb_highacc
-path: cosmopower/trained_models/camb_highacc/
-
-emulated_code:
-  name: camb
-  version: "1.5.0"
-  extra_args:
-    lens_potential_accuracy: 8
-    kmax: 10.0
-    k_per_logint: 130
-    lens_margin: 2050
-    AccuracyBoost: 1.0
-    lAccuracyBoost: 1.2
-    lSampleBoost: 1.0
-    DoLateRadTruncation: false
-    min_l_logl_sampling: 6000
-
-samples:
-  Ntraining: 150000
-
-  parameters:
-    ombh2:
-      range: [0.01,0.05]
-    omch2:
-      range: [0.05,0.50]
-    logA:
-      range: [2.0,4.0]
-      drop: True
-    As:
-      derived: True
-      value: "lambda logA: 1.e-10 * np.exp(logA)"
-    tau:
-      range: [0.02, 0.12]
-    ns:
-      range: [0.8, 1.2]
-    h:
-      range: [0.4,1.0]
-      drop: True
-    H0:
-      derived: True
-      value: "lambda h: h * 100.0"
-
-networks:
-  - quantity: "Cl/tt"
-    ell_factor: True
-    inputs: [ ombh2, omch2, logA, tau, ns, h ]
-    type: NN
-    log: True
-    modes:
-      label: l
-      range: [2,10000]
-    n_traits:
-      n_hidden: [ 512, 512, 512, 512 ]
-    training:
-      validation_split: 0.1
-      learning_rates: [ 1.e-2, 1.e-3, 1.e-4, 1.e-5, 1.e-6, 1.e-7 ]
-      batch_sizes: [ 1000, 2000, 5000, 10000, 20000, 50000 ]
-      gradient_accumulation_steps: [ 1, 1, 1, 1, 1, 1 ]
-      patience_values: [ 100, 100, 100, 100, 100, 100 ]
-      max_epochs: [ 1000, 1000, 1000, 1000, 1000, 1000 ]
-
-  - quantity: "Cl/te"
-    ell_factor: True
-    inputs: [ ombh2, omch2, logA, tau, ns, h ]
-    type: PCAplusNN
-    log: False
-    modes:
-      label: l
-      range: [2,10000]
-    p_traits:
-      n_pcas: 512
-      n_batches: 10
-    n_traits:
-      n_hidden: [ 512, 512, 512, 512 ]
-    training:
-      validation_split: 0.1
-      learning_rates: [ 1.e-2, 1.e-3, 1.e-4, 1.e-5, 1.e-6, 1.e-7 ]
-      batch_sizes: [ 1000, 2000, 5000, 10000, 20000, 50000 ]
-      gradient_accumulation_steps: [ 1, 1, 1, 1, 1, 1 ]
-      patience_values: [ 100, 100, 100, 100, 100, 100 ]
-      max_epochs: [ 1000, 1000, 1000, 1000, 1000, 1000 ]
-
-  - quantity: "Cl/ee"
-    ell_factor: True
-    inputs: [ ombh2, omch2, logA, tau, ns, h ]
-    type: NN
-    log: True
-    modes:
-      label: l
-      range: [2,10000]
-    n_traits:
-      n_hidden: [ 512, 512, 512, 512 ]
-    training:
-      validation_split: 0.1
-      learning_rates: [ 1.e-2, 1.e-3, 1.e-4, 1.e-5, 1.e-6, 1.e-7 ]
-      batch_sizes: [ 1000, 2000, 5000, 10000, 20000, 50000 ]
-      gradient_accumulation_steps: [ 1, 1, 1, 1, 1, 1 ]
-      patience_values: [ 100, 100, 100, 100, 100, 100 ]
-      max_epochs: [ 1000, 1000, 1000, 1000, 1000, 1000 ]
-  
-  - quantity: "Cl/bb"
-    ell_factor: True
-    inputs: [ ombh2, omch2, logA, tau, ns, h ]
-    type: NN
-    log: True
-    modes:
-      label: l
-      range: [2,10000]
-    n_traits:
-      n_hidden: [ 512, 512, 512, 512 ]
-    training:
-      validation_split: 0.1
-      learning_rates: [ 1.e-2, 1.e-3, 1.e-4, 1.e-5, 1.e-6, 1.e-7 ]
-      batch_sizes: [ 1000, 2000, 5000, 10000, 20000, 50000 ]
-      gradient_accumulation_steps: [ 1, 1, 1, 1, 1, 1 ]
-      patience_values: [ 100, 100, 100, 100, 100, 100 ]
-      max_epochs: [ 1000, 1000, 1000, 1000, 1000, 1000 ]
-  
-  - quantity: "Cl/pp"
-    ell_factor: True
-    inputs: [ ombh2, omch2, logA, ns, h ]
-    type: PCAplusNN
-    log: True
-    modes:
-      label: l
-      range: [2,10000]
-    p_traits:
-      n_pcas: 64
-      n_batches: 10
-    n_traits:
-      n_hidden: [ 512, 512, 512, 512 ]
-    training:
-      validation_split: 0.1
-      learning_rates: [ 1.e-2, 1.e-3, 1.e-4, 1.e-5, 1.e-6, 1.e-7 ]
-      batch_sizes: [ 1000, 2000, 5000, 10000, 20000, 50000 ]
-      gradient_accumulation_steps: [ 1, 1, 1, 1, 1, 1 ]
-      patience_values: [ 100, 100, 100, 100, 100, 100 ]
-      max_epochs: [ 1000, 1000, 1000, 1000, 1000, 1000 ]
diff --git a/demos/camb_highacc_pknl.yaml b/demos/camb_highacc_pknl.yaml
deleted file mode 100644
index 18f4c50..0000000
--- a/demos/camb_highacc_pknl.yaml
+++ /dev/null
@@ -1,78 +0,0 @@
-network_name: camb_highacc
-path: cosmopower/trained_models/camb_highacc_pknl/
-
-emulated_code:
-  name: camb
-  version: "1.5.0"
-  inputs: [ z, ombh2, omch2, As, tau, ns, H0, NonLinearModel.HMCode_A_baryon, NonLinearModel.HMCode_eta_baryon, NonLinearModel.HMCode_logT_AGN ]
-  extra_args:
-    lmax: 6000
-    lens_potential_accuracy: 8
-    kmax: 50.0
-    k_per_logint: 130
-    lens_margin: 2050
-    AccuracyBoost: 1.0
-    lAccuracyBoost: 1.2
-    lSampleBoost: 1.0
-    DoLateRadTruncation: false
-    min_l_logl_sampling: 6000
-    NonLinearModel.Min_kh_nonlinear: 5.e-5
-    NonLinearModel.halofit_version: 'mead2020'
-
-samples:
-  Ntraining: 150000
-
-  parameters:
-    z: [0.0, 5.0]
-    ombh2: [0.01,0.05]
-    omch2: [0.05,0.50]
-    logA: [2.0,4.0]
-    As: "lambda logA: 1.e-10 * np.exp(logA)"
-    tau: [0.02, 0.12]
-    ns: [0.8, 1.2]
-    h: [0.4,1.0]
-    H0: "lambda h: h * 100.0"
-    NonLinearModel.HMCode_A_baryon: [2.0, 4.0] 
-    NonLinearModel.HMCode_eta_baryon: [0.5, 1.0]
-    NonLinearModel.HMCode_logT_AGN: [7.3, 8.3]
-
-networks:
-  - quantity: "Pk/nonlin"
-    ell_factor: False
-    inputs: [ ombh2, omch2, logA, tau, ns, h, z, NonLinearModel.HMCode_A_baryon,  NonLinearModel.HMCode_eta_baryon, NonLinearModel.HMCode_logT_AGN]
-    type: NN
-    log: True
-    modes:
-      label: k
-      range: [5.e-5,50]
-      spacing: log
-      steps: 128
-    n_traits:
-      n_hidden: [ 512, 512, 512, 512 ]
-    training:
-      validation_split: 0.1
-      learning_rates: [ 1.e-2, 1.e-3, 1.e-4, 1.e-5, 1.e-6, 1.e-7 ]
-      batch_sizes: [ 1000, 2000, 5000, 10000, 20000, 50000 ]
-      gradient_accumulation_steps: [ 1, 1, 1, 1, 1, 1 ]
-      patience_values: [ 100, 100, 100, 100, 100, 100 ]
-      max_epochs: [ 1000, 1000, 1000, 1000, 1000, 1000 ]
-
-  - quantity: "Pk/nlboost"
-    ell_factor: False
-    inputs: [ ombh2, omch2, logA, tau, ns, h, z, NonLinearModel.HMCode_A_baryon,  NonLinearModel.HMCode_eta_baryon, NonLinearModel.HMCode_logT_AGN]
-    type: NN
-    log: False
-    modes:
-      label: k
-      range: [5.e-5,50]
-      spacing: log
-      steps: 128
-    n_traits:
-      n_hidden: [ 512, 512, 512, 512 ]
-    training:
-      validation_split: 0.1
-      learning_rates: [ 1.e-2, 1.e-3, 1.e-4, 1.e-5, 1.e-6, 1.e-7 ]
-      batch_sizes: [ 1000, 2000, 5000, 10000, 20000, 50000 ]
-      gradient_accumulation_steps: [ 1, 1, 1, 1, 1, 1 ]
-      patience_values: [ 100, 100, 100, 100, 100, 100 ]
-      max_epochs: [ 1000, 1000, 1000, 1000, 1000, 1000 ]
diff --git a/demos/demo_cmb_bao.yaml b/demos/demo_cmb_bao.yaml
new file mode 100644
index 0000000..986c218
--- /dev/null
+++ b/demos/demo_cmb_bao.yaml
@@ -0,0 +1,80 @@
+# Set the location for the sampler outputs
+# (samples, restart, progress files etc).
+output: output/demo_cmb_bao
+
+
+# Set the parameter ranges and priors to sample over
+params:
+  ombh2:
+    prior:
+      min: 0.01
+      max: 0.05
+    ref: 0.0225
+  omch2:
+    prior:
+      min: 0.01
+      max: 0.90
+    ref: 0.120
+  h:
+    prior:
+      min: 0.4
+      max: 1.0
+    ref: 0.7
+  H0:
+    value: "lambda h: 100.0 * h"
+  logA:
+    prior:
+      min: 2.0
+      max: 4.0
+    ref: 3.05
+  As:
+    value: "lambda logA: 1e-10 * np.exp(logA)"
+  ns:
+    prior:
+      min: 0.8
+      max: 1.2
+    ref: 0.97
+  tau:
+    prior:
+      min: 0.01
+      max: 0.8
+    ref: 0.054
+
+
+# Add an extra prior e.g. this one on tau.
+priors:
+  tau_prior: "stats.norm.logpdf(tau, loc = 6.7e-2, scale = 2.3e-2)"
+
+
+# For testing use evaluate (likelihood calculated at one reference point).
+# Otherwise mcmc.
+sampler:
+  # mcmc:
+  evaluate:
+
+# Specify the Theory components to use. Here we use cosmpower for the Cl observables
+# but run normal CAMB in the background-only mode (which is fast) for background observables.
+# Note we use the 'provides' key within each theory, to specify which observables come from where
+# (in some cases observables can be available from more than one Theory).
+theory:
+  cosmopower:
+    package_file: ../cosmopower-organization/jense_2024_emulators/jense_2023_cmb_camb_lcdm.yaml
+    root_dir: ../cosmopower-organization/jense_2024_emulators/
+    provides: # Force cobaya to use the cosmopower-calculated versions of these
+      Cl:
+        tt:
+        te:
+        ee:
+        pp:
+  camb:
+    provides: # Force cobaya to use the camb-calculated versions of these
+      angular_diameter_distance:
+      Hubble:
+      fsigma8:
+
+# Include a BAO and CMB TTTEEE likelihood
+likelihood:
+  bao.sdss_dr12_consensus_final:
+    stop_at_error: True
+  planck_2018_highl_plik.TTTEEE_lite_native:
+    stop_at_error: True
\ No newline at end of file
diff --git a/demos/demo_cp_ext_bgd.yaml b/demos/demo_cp_ext_bgd.yaml
deleted file mode 100644
index b7a86ef..0000000
--- a/demos/demo_cp_ext_bgd.yaml
+++ /dev/null
@@ -1,66 +0,0 @@
-output: output/democp
-
-params:
-  ombh2:
-    prior:
-      min: 0.01
-      max: 0.05
-    ref: 0.0225
-  omch2:
-    prior:
-      min: 0.01
-      max: 0.90
-    ref: 0.120
-  h:
-    prior:
-      min: 0.4
-      max: 1.0
-    ref: 0.7
-  H0:
-    value: "lambda h: 100.0 * h"
-  logA:
-    prior:
-      min: 2.0
-      max: 4.0
-    ref: 3.05
-  As:
-    value: "lambda logA: 1e-10 * np.exp(logA)"
-  ns:
-    prior:
-      min: 0.8
-      max: 1.2
-    ref: 0.97
-  tau:
-    prior:
-      min: 0.01
-      max: 0.8
-    ref: 0.054
-
-priors:
-  tau_prior: "stats.norm.logpdf(tau, loc = 6.7e-2, scale = 2.3e-2)"
-
-sampler:
-  mcmc:
-
-theory:
-  wrappers.cobaya.CosmoPower:
-    package_file: demos/camb_highacc.yaml
-    provides:
-      Cl:
-        tt:
-        te:
-        ee:
-        pp:
-  camb:
-    extra_args:
-      lens_potential_accuracy: 8
-    provides:
-      angular_diameter_distance:
-      Hubble:
-      fsigma8:
-
-likelihood:
-  bao.sdss_dr12_consensus_final:
-    stop_at_error: True
-  planck_2018_highl_plik.TTTEEE_lite_native:
-    stop_at_error: True
diff --git a/requirements.txt b/requirements.txt
index 49d30cf..25b09ba 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -10,3 +10,4 @@ pyDOE
 h5py
 tensorflow>=2.18
 tensorflow_probability>=0.24
+tf-keras
\ No newline at end of file