diff --git a/README.md b/README.md
index 2446149..da87fbd 100644
--- a/README.md
+++ b/README.md
@@ -6,16 +6,18 @@
 
 [![Build Status](https://travis-ci.org/supernnova/SuperNNova.svg?branch=master)](https://travis-ci.org/supernnova/SuperNNova)
 
+```bash
+A new realease of SuperNNova is in the main branch. For DES-5yr analysis please use the branch SNANA_DES5yr (and any other analysis using the syntax: python run.py)
+```
 
 ### What is SuperNNova (SNN)
 
-SuperNNova is an open-source photoemtric time-series classification framework.
+SuperNNova is an open-source photometric time-series classification framework.
 
 The framework includes different RNN architectures (LSTM, GRU, Bayesian RNNs) and can be trained with simulations in `.csv` and `SNANA FITS` format. SNN is part of the [PIPPIN](https://github.com/dessn/Pippin) end-to-end cosmology pipeline.
 
 You can train your own model for time-series classification (binary or multi-class) using photometry and additional features.
 
-
 Please include the full citation if you use this material in your research: [A Möller and T de Boissière,
 MNRAS, Volume 491, Issue 3, January 2020, Pages 4277–4293.](https://academic.oup.com/mnras/article-abstract/491/3/4277/5651173)
 
diff --git a/docs/conf.py b/docs/conf.py
index a5685db..5836f04 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -17,14 +17,14 @@
 # -- Project information -----------------------------------------------------
 
 project = "supernnova"
-copyright = "2023, Anais Moller and Thibault de Boissiere"
+copyright = "2025, Anais Moller and Thibault de Boissiere"
 author = "Anais Moller and Thibault de Boissiere"
 
 # The short X.Y version
-version = "0.0.0-dev"
+version = "v3.0.19"
 
 # The full version, including alpha/beta/rc tags
-release = "0.0.0-dev"
+release = "v3.0.19"
 
 
 # -- General configuration ---------------------------------------------------
diff --git a/docs/installation/FAQ.rst b/docs/installation/FAQ.rst
index be806a9..6558c6e 100644
--- a/docs/installation/FAQ.rst
+++ b/docs/installation/FAQ.rst
@@ -11,17 +11,21 @@ SuperNNova is a framework for lightcurve classification which uses supervised le
 
 - **Do you have a paper describing SuperNNova? How can I cite you?**
 
-The paper has been published by `MNRAS`_. A copy of the paper can be found here `ArXiv`_. 
+The SuperNNova paper has been published by `MNRAS`_. A copy of the paper can be found here `ArXiv`_. 
 
 - **How can I install it?**
 
-You can ``clone`` our `GitHub`_. Beware, the supported version of GitHub repository is this `GitHub`_!!!! (previous version was hosted in a different webpage). Previous pip installation it is not updated. If you have a compelling case to bring it back let me know! SuperNNova works in Unix based systems only.
+You can ``clone`` our `GitHub`_. Beware, the supported version of GitHub repository is this `GitHub`_!!!! (previous version was hosted in a different webpage). If you have a compelling case to bring it back let me know! SuperNNova works in Unix based systems only.
+
+- **I have installed SuperNNova previously and I can't find the previous documentation**
+
+At the end of 2024 we have released a new version of SuperNNova with updated pytorch libraries and a new structure. The previous code can be found in the `GitHub`_ repository under the branch ``SNANA_DES5yr``. To access the documentation `_SNANADes5yr docs_`.
 
 - **What data do I need?**
 
-You only need lightcurves (photometric time-series) to use SuperNNova. Additional information can be added as well. E.g. we used supernova host-galaxy redshifts in the paper.
+You only need lightcurves (photometric time-series) to use SuperNNova. Additional information can be added as well. E.g. we used supernova host-galaxy redshifts. If you use redshifts and want to do a cosmology analysis you should also use the norm `cosmo_quantile` to avoid biases (see the paper `5-year photometric sample`)
 
-- **Is the data used in the paper publicly available?**
+- **Is the data used in the 2020 paper publicly available?**
 
 Yes it is! `SuperNNovaSimulations`_
 We want to foster reproducibility so you can copy the data and reproduce all our experiments with ``run_paper.py`` in the ``paper`` branch. Beware, it will take while!
@@ -85,6 +89,7 @@ Check that you provided the appropriate ``raw_dir`` and that the files are eithe
 
 .. _ArXiv: https://arxiv.org/abs/1901.06384
 .. _MNRAS: https://academic.oup.com/mnras/advance-article-abstract/doi/10.1093/mnras/stz3312/5651173
+.. _SNANADes5yr docs: https://supernnova.readthedocs.io/snana_des5yr/index.html
 .. _SuperNNovaSimulations: https://zenodo.org/record/3265189#.XRo2mS2B1TY
 .. _Fortunato et al 2017: https://arxiv.org/abs/1704.02798
 .. _Gal et Ghahramani 2015: https://arxiv.org/abs/1506.02142