diff --git a/nbs/docs/contribute/step-by-step.md b/nbs/docs/contribute/step-by-step.md
index 49848ff9d..36440ce5f 100644
--- a/nbs/docs/contribute/step-by-step.md
+++ b/nbs/docs/contribute/step-by-step.md
@@ -1,6 +1,7 @@
-# Step-by-step Contribution Guide
-
-> This document contains instructions for collaborating on the different libraries of Nixtla.
+---
+title: Step-by-step Contribution Guide
+description: This document contains instructions for collaborating on the different libraries of Nixtla.
+---
 
 Sometimes, diving into a new technology can be challenging and overwhelming. We've been there too, and we're more than ready to assist you with any issues you may encounter while following these steps. Don't hesitate to reach out to us on [Slack](https://join.slack.com/t/nixtlacommunity/shared_invite/zt-1pmhan9j5-F54XR20edHk0UtYAPcW4KQ). Just give fede a ping, and she'll be glad to assist you.
 
diff --git a/nbs/docs/contribute/techstack.md b/nbs/docs/contribute/techstack.md
index 89265f8e1..5cd1b3178 100644
--- a/nbs/docs/contribute/techstack.md
+++ b/nbs/docs/contribute/techstack.md
@@ -1,4 +1,7 @@
-# Contributing Code to Nixtla Development
+---
+title: Contributing Code to Nixtla Development
+description: A guide on the technical skills and tools needed to contribute code to the Nixtla project.
+---
 
 Curious about the skills required to contribute to the Nixtla project?
 
diff --git a/nbs/src/core/models_intro.qmd b/nbs/src/core/models_intro.qmd
index 0ff58a8b1..d5ded7f62 100644
--- a/nbs/src/core/models_intro.qmd
+++ b/nbs/src/core/models_intro.qmd
@@ -8,10 +8,10 @@ Automatic forecasting tools search for the best parameters and select the best p
 
 |Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |
 |:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:|
-|[`AutoARIMA`](../../models.html#autoarima)|✅|✅|✅|✅|✅|
-|[`AutoETS`](../../models.html#autoets)|✅|✅|✅|✅|✅|
-|[`AutoCES`](../../models.html#autoces)|✅|✅|✅|✅||
-|[`AutoTheta`](../../models.html#autotheta)|✅|✅|✅|✅|✅|
+|[`AutoARIMA`](models.html#autoarima)|✅|✅|✅|✅|✅|
+|[`AutoETS`](models.html#autoets)|✅|✅|✅|✅|✅|
+|[`AutoCES`](models.html#autoces)|✅|✅|✅|✅||
+|[`AutoTheta`](models.html#autotheta)|✅|✅|✅|✅|✅|
 
 
 ## ARIMA Family
@@ -19,8 +19,8 @@ These models exploit the existing autocorrelations in the time series.
 
 |Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |
 |:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:|
-|[`ARIMA`](../../models.html#arima)|✅|✅|✅|✅|✅|
-|[`AutoRegressive`](../../models.html#autoregressive)|✅|✅|✅|✅|✅|
+|[`ARIMA`](models.html#arima)|✅|✅|✅|✅|✅|
+|[`AutoRegressive`](models.html#autoregressive)|✅|✅|✅|✅|✅|
 
 
 ## Theta Family
@@ -28,10 +28,10 @@ Fit two theta lines to a deseasonalized time series, using different techniques
 
 |Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |
 |:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:|
-|[`Theta`](../../models.html#theta)|✅|✅|✅|✅|✅|
-|[`OptimizedTheta`](../../models.html#optimizedtheta)|✅|✅|✅|✅|✅|
-|[`DynamicTheta`](../../models.html#dynamictheta)|✅|✅|✅|✅|✅|
-|[`DynamicOptimizedTheta`](../../models.html#dynamicoptimizedtheta)|✅|✅|✅|✅|✅|
+|[`Theta`](models.html#theta)|✅|✅|✅|✅|✅|
+|[`OptimizedTheta`](models.html#optimizedtheta)|✅|✅|✅|✅|✅|
+|[`DynamicTheta`](models.html#dynamictheta)|✅|✅|✅|✅|✅|
+|[`DynamicOptimizedTheta`](models.html#dynamicoptimizedtheta)|✅|✅|✅|✅|✅|
 
 
 ## Multiple Seasonalities
@@ -39,7 +39,7 @@ Suited for signals with more than one clear seasonality. Useful for low-frequenc
 
 |Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |
 |:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:|
-|[`MSTL`](../../models.html#mstl)|✅|✅|✅|✅|✅|
+|[`MSTL`](models.html#mstl)|✅|✅|✅|✅|✅|
 
 
 ## GARCH and ARCH Models
@@ -47,8 +47,8 @@ Suited for modeling time series that exhibit non-constant volatility over time.
 
 |Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |
 |:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:|
-|[`GARCH`](../../models.html#garch)|✅|✅|✅|✅|✅|
-|[`ARCH`](../../models.html#arch)|✅|✅|✅|✅|✅|
+|[`GARCH`](models.html#garch)|✅|✅|✅|✅|✅|
+|[`ARCH`](models.html#arch)|✅|✅|✅|✅|✅|
 
 
 ## Baseline Models
@@ -56,12 +56,12 @@ Classical models for establishing baseline.
 
 |Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |
 |:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:|
-|[`HistoricAverage`](../../models.html#historicaverage)|✅|✅|✅|✅|✅|
-|[`Naive`](../../models.html#naive)|✅|✅|✅|✅|✅|
-|[`RandomWalkWithDrift`](../../models.html#randomwalkwithdrift)|✅|✅|✅|✅|✅|
-|[`SeasonalNaive`](../../models.html#seasonalnaive)|✅|✅|✅|✅|✅|
-|[`WindowAverage`](../../models.html#windowaverage)|✅|||||
-|[`SeasonalWindowAverage`](../../models.html#seasonalwindowaverage)|✅|||||
+|[`HistoricAverage`](models.html#historicaverage)|✅|✅|✅|✅|✅|
+|[`Naive`](models.html#naive)|✅|✅|✅|✅|✅|
+|[`RandomWalkWithDrift`](models.html#randomwalkwithdrift)|✅|✅|✅|✅|✅|
+|[`SeasonalNaive`](models.html#seasonalnaive)|✅|✅|✅|✅|✅|
+|[`WindowAverage`](models.html#windowaverage)|✅|||||
+|[`SeasonalWindowAverage`](models.html#seasonalwindowaverage)|✅|||||
 
 
 ## Exponential Smoothing
@@ -69,10 +69,10 @@ Uses a weighted average of all past observations where the weights decrease expo
 
 |Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |
 |:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:|
-|[`SimpleExponentialSmoothing`](../../models.html#simpleexponentialsmoothing)|✅|||||
-|[`SimpleExponentialSmoothingOptimized`](../../models.html#simpleexponentialsmoothingoptimized)|✅|||||
-|[`Holt`](../../models.html#holt)|✅|✅|✅|✅|✅|
-|[`HoltWinters`](../../models.html#holtwinters)|✅|✅|✅|✅|✅|
+|[`SimpleExponentialSmoothing`](models.html#simpleexponentialsmoothing)|✅|||||
+|[`SimpleExponentialSmoothingOptimized`](models.html#simpleexponentialsmoothingoptimized)|✅|||||
+|[`Holt`](models.html#holt)|✅|✅|✅|✅|✅|
+|[`HoltWinters`](models.html#holtwinters)|✅|✅|✅|✅|✅|
 
 
 ## Sparse or Intermittent 
@@ -80,12 +80,12 @@ Suited for series with very few non-zero observations
 
 |Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |
 |:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:|
-|[`ADIDA`](../../models.html#adida)|✅|||||
-|[`CrostonClassic`](../../models.html#crostonclassic)|✅|||||
-|[`CrostonOptimized`](../../models.html#crostonoptimized)|✅|||||
-|[`CrostonSBA`](../../models.html#crostonsba)|✅|||||
-|[`IMAPA`](../../models.html#imapa)|✅|||||
-|[`TSB`](../../models.html#tsb)|✅|||||
+|[`ADIDA`](models.html#adida)|✅|||||
+|[`CrostonClassic`](models.html#crostonclassic)|✅|||||
+|[`CrostonOptimized`](models.html#crostonoptimized)|✅|||||
+|[`CrostonSBA`](models.html#crostonsba)|✅|||||
+|[`IMAPA`](models.html#imapa)|✅|||||
+|[`TSB`](models.html#tsb)|✅|||||