diff --git a/README.md b/README.md
index 203e8fb..bf9dabf 100644
--- a/README.md
+++ b/README.md
@@ -1,8 +1,6 @@
-# Structured State Spaces for Sequence Modeling
+# Liquid State Space Models
 
-This repository provides implementations and experiments for the following papers.
-
-## Liquid S4
+This repository provides implementations and experiments for Liquid S4
 
 ```bash
 python3 -m train wandb=null experiment=s4-lra-cifar-new # plain S4
@@ -11,34 +9,6 @@ python3 -m train wandb=null experiment=s4-lra-cifar-new model.layer.liquid=2 # l
 python3 -m train wandb=null experiment=s4-lra-cifar-new model.layer.liquid=N # liquid S4 with N terms u(i)*u(j)...
 ```
 
-## SaShiMi (arXiv)
-
-![SaShiMi](assets/sashimi.png "SaShiMi Architecture")
-> **It's Raw! Audio Generation with State-Space Models**\
-> Karan Goel, Albert Gu, Chris Donahue, Christopher Ré\
-> Paper: https://arxiv.org/abs/2202.09729
-
-## S4 (ICLR 2022 - Outstanding Paper HM)
-
-![Structured State Spaces](assets/properties.png "Properties of Structured State Spaces")
-> **Efficiently Modeling Long Sequences with Structured State Spaces**\
-> Albert Gu, Karan Goel, Christopher Ré\
-> Paper: https://arxiv.org/abs/2111.00396
-
-## LSSL (NeurIPS 2021)
-
-![Linear State Space Layer](assets/splash.png "Properties of Sequential State Spaces")
-> **Combining Recurrent, Convolutional, and Continuous-time Models with the Linear State Space Layer**\
-> Albert Gu, Isys Johnson, Karan Goel, Khaled Saab, Tri Dao, Atri Rudra, Christopher Ré\
-> Paper: https://arxiv.org/abs/2110.13985
-
-## HiPPO (NeurIPS 2020 - Spotlight)
-![HiPPO Framework](assets/hippo.png "HiPPO Framework")
-> **HiPPO: Recurrent Memory with Optimal Polynomial Projections**\
-> Albert Gu*, Tri Dao*, Stefano Ermon, Atri Rudra, Christopher Ré\
-> Paper: https://arxiv.org/abs/2008.07669
-
-
 ## Table of Contents
 - [Repository Setup](#setup)
 - S4
@@ -49,26 +19,6 @@ python3 -m train wandb=null experiment=s4-lra-cifar-new model.layer.liquid=N # l
 - [Repository Structure](#overall-repository-structure)
 - [Citation](#citation)
 
-## Changelog
-
-### 2022-05-01 - [V2.1]
-- Minor updates to S4 modules
-- New S4D (S4-diagonal) standalone model found at `src/models/sequence/ss/standalone/s4d.py`. Simple variant using diagonal SSMs that recovers S4's performance on most tasks. Can be run with any existing experiment config with the additional flag `model/layer=s4d` on the command line.
-- New [LRA configs](#long-range-arena-lra) for updated S4 code, with an average score of ~86
-
-### 2022-04-03
-By default, S4 no longer requires installing Pykeops or a custom CUDA kernel.
-
-### 2022-02-27 - [V2]
-Code release for SaShiMi audio model.
-
-### 2022-01-29
-Added configs for time series datasets from the Informer paper.
-
-### 2021-11-18 - [V1]
-First release of this repository containing the S4 module and configs to reproduce sCIFAR, Speech Commands, Long Range Arena, and WikiText-103 results.
-
-
 ## Setup
 
 ### Requirements
@@ -372,4 +322,4 @@ If you use this codebase, or otherwise found our work valuable, please cite:
   volume={33},
   year={2020}
 }
-```
\ No newline at end of file
+```
diff --git a/deploy.py b/deploy.py
deleted file mode 100644
index 1dfbb71..0000000
--- a/deploy.py
+++ /dev/null
@@ -1,73 +0,0 @@
-import os
-import sys
-
-base_source_dirs = {
-    "mathias-thinkpad": "/home/mathias/dev",
-    "mathias-desktop": "/home/mathias/dev/ist",
-}
-base_move_dirs = {
-    "mathias-thinkpad": "/home/mathias/dev/azure",
-    "deephawk": "/home/mathias/",
-    "mathias-desktop": "/home/mathias/dev/azure/",
-}
-
-base_target_dirs = {
-    "deephawk": "deephawk/dev/",
-    "pixie": "deephawk/dev/azure/pixie",
-    "hpc": "hpc",
-    "z1": "deephawk/dev/azure/z1",
-    "z2": "deephawk/dev/azure/z2",
-    "rpc": "rpc",
-    "1080": "deephawk/dev/azure/1080",
-    "deepart": "deephawk/dev/azure/deepart",
-}
-
-sync_dirs = [
-    "s4",
-    "s4/configs/experiment",
-    "s4/configs/model/layer",
-    "s4/src/models/hippo",
-    "s4/src/models/sequence/ss",
-]
-sync_ext = [".py", ".sh", ".sl", ".yaml"]
-
-
-host = os.uname()[1].lower()
-if not host in base_move_dirs or not host in base_move_dirs:
-    print("Unknown host: " + host)
-    sys.exit(-1)
-
-target = "deephawk"
-if len(sys.argv) > 1:
-    target = sys.argv[1].lower()
-if not target in base_target_dirs:
-    print("Unknown target: " + target)
-    sys.exit(-1)
-
-flags = "ruv"
-if len(sys.argv) > 2:
-    if sys.argv[2].lower() == "--force":
-        flags = "rv"
-        print("Force rewrite")
-
-print('Syncing from "' + host + '" to "' + target + '"')
-for sync_dir in sync_dirs:
-    print('   moving dir "' + sync_dir + '"')
-    source_dir = os.path.join(base_source_dirs[host], sync_dir)
-
-    target_dir = os.path.join(base_move_dirs[host], base_target_dirs[target], sync_dir)
-
-    print("Source dir: " + str(source_dir))
-    print("Target dir: " + str(target_dir))
-    cmd_str = (
-        "rsync -"
-        + flags
-        + " '"
-        + source_dir
-        + "/' --exclude '.git' --include '*.py' --include '*.yaml' --include '*.sl' --include '*.sh' --exclude '*'  '"
-        + target_dir
-        + "/'"
-    )
-
-    os.system(cmd_str)
-    # print(cmd_str)
\ No newline at end of file
diff --git a/run_local.sh b/run_local.sh
deleted file mode 100644
index a06d8f1..0000000
--- a/run_local.sh
+++ /dev/null
@@ -1,9 +0,0 @@
-# papeR: S4_v1: 87.26, v2: 88.5, liquid: 90.95
-python3 -m train wandb=null experiment=s4-lra-cifar-new # 4.2/it/s  top score:  0.8600000143051147
-python3 -m train wandb=null experiment=s4-lra-cifar-new model.layer.liquid=1 # 3.04 it/s
-python3 -m train wandb=null experiment=s4-lra-cifar-new model.layer.liquid=2 # 2.91 it/s top score:  0.8611999750137329
-python3 -m train wandb=null experiment=s4-lra-cifar-new model.layer.liquid=3 # it/s
-python3 -m train wandb=null experiment=s4-lra-cifar-new optimizer.weight_decay=0.01 trainer.max_epochs=200
-python3 -m train wandb=null experiment=s4-lra-cifar-new optimizer.weight_decay=0.01 trainer.max_epochs=300
-python3 -m train wandb=null experiment=s4-lra-cifar-new model.d_model=192 model.layer.postact=glu model.layer.bidirectional=true optimizer.weight_decay=0.01 trainer.max_epochs=200
-python3 -m train wandb=null experiment=s4-lra-cifar-new optimizer.lr=0.01 optimizer.weight_decay=0.01 trainer.max_epochs=200
\ No newline at end of file
diff --git a/run_sl1.sl b/run_sl1.sl
deleted file mode 100644
index 1533eb7..0000000
--- a/run_sl1.sl
+++ /dev/null
@@ -1,100 +0,0 @@
-#!/bin/bash
-#
-#SBATCH --job-name=s4_3
-#SBATCH --output=s4_3.txt
-#
-#number of CPUs to be used
-#SBATCH --ntasks=1
-#SBATCH -c 8
-#
-#Define the number of hours the job should run.
-#Maximum runtime is limited to 10 days, ie. 240 hours
-#SBATCH --time=128:00:00
-#
-#Define the amount of system RAM used by your job in GigaBytes
-#SBATCH --mem=128G
-
-#SBATCH --partition=gpu
-#SBATCH --gres=gpu:2
-#SBATCH --constraint=A10
-
-#Send emails when a job starts, it is finished or it exits
-#SBATCH --mail-user=mlechner@ist.ac.at
-#SBATCH --mail-type=END,FAIL
-#
-#SBATCH --no-requeue
-#SBATCH --export=NONE
-unset SLURM_EXPORT_ENV
-
-
-module load python/3.8.12
-module load cuda/11.2.2
-module load cudnn/8.2.4.15
-#module load cudnn/8.1.0.77
-#module load cudnn/8.1.1.33
-
-cd $HOME/s4
-source venv/bin/activate
-
-
-# mkdir -p $HOME/s4/tmp/tmp_$SLURM_ARRAY_TASK_ID
-# export TMPDIR=$HOME/s4/tmp/tmp_$SLURM_ARRAY_TASK_ID
-# python3 -m train model.layer.poly=true pipeline=mnist model=s4 
-### S4 Paper:
-# Listops 58.35
-# IMDB: 76.02
-# AAN: 87.09
-# CIFAR: 87.26
-# Pathfinder: 86.05
-# Path-X: 88.10
-
-# listops: test_acc: 0.567
-# imdb: 0.76816
-# pathfindeR: 0.495
-
-# 4 gpus:
-# listops best 0.54650
-# imdb  0.74128
-# cifar: 0.84100
-# pathfindeR 0.75270
-
-python3 -m train wandb=null experiment=s4-lra-cifar-new trainer.gpus=2 model.layer.liquid=3 # it/s
-
-
-# trainer.gpus=2
-#  lr: 0.004
-#  weight_decay: 0.03
-#python -m train wandb=null experiment=s4-lra-pathfinder-new optimizer.lr=0.008 optimizer.weight_decay=0.01 trainer.gpus=4
-#python -m train wandb=null experiment=s4-lra-pathfinder-new optimizer.lr=0.01 optimizer.weight_decay=0.03 trainer.gpus=4
-#python -m train wandb=null experiment=s4-lra-pathfinder-new optimizer.lr=0.008 model.layer.d_state=128 trainer.gpus=4
-
-
-#if [ $SLURM_ARRAY_TASK_ID = 6 ]
-#then
-#  python -m train model.layer.poly=true experiment=s4-lra-cifar model.layer.postact=glu model.layer.bidirectional=true optimizer.weight_decay=0.01 trainer.max_epochs=160
-#elif [ $SLURM_ARRAY_TASK_ID = 7 ]
-#then
-#  python -m train model.layer.poly=true experiment=s4-lra-cifar model.layer.postact=glu model.layer.bidirectional=true optimizer.weight_decay=0.005 trainer.max_epochs=160
-#  python -m train experiment=s4-lra-cifar model.layer.postact=glu model.layer.bidirectional=true optimizer.weight_decay=0.005 trainer.max_epochs=160
-#elif [ $SLURM_ARRAY_TASK_ID = 8 ]
-#then
-#  python -m train model.layer.poly=true experiment=s4-lra-cifar model.layer.postact=glu model.layer.bidirectional=true optimizer.weight_decay=0.01 trainer.max_epochs=160 scheduler.patience=20
-#elif [ $SLURM_ARRAY_TASK_ID = 9 ]
-#then
-#  python -m train model.layer.poly=true experiment=s4-lra-pathfinder
-#  python -m train experiment=s4-lra-pathfinder
-#elif [ $SLURM_ARRAY_TASK_ID = 10 ]
-#then
-#  python -m train model.layer.poly=true experiment=s4-lra-pathfinder optimizer.lr=0.01
-#elif [ $SLURM_ARRAY_TASK_ID = 11 ]
-#then
-#  python -m train model.layer.poly=true experiment=s4-lra-pathfinder optimizer.lr=0.006
-#elif [ $SLURM_ARRAY_TASK_ID = 12 ]
-#then
-#  python -m train model.layer.poly=true experiment=s4-lra-pathfinder optimizer.lr=0.004 scheduler.patience=20
-#elif [ $SLURM_ARRAY_TASK_ID = 13 ]
-#then
-#  python -m train model.layer.poly=true experiment=s4-lra-aan &> results/aan.txt
-#fi
-
-# rm -r $HOME/s4/tmp/tmp_$SLURM_ARRAY_TASK_ID
\ No newline at end of file
diff --git a/run_sl2.sl b/run_sl2.sl
deleted file mode 100644
index 2f3ed1d..0000000
--- a/run_sl2.sl
+++ /dev/null
@@ -1,46 +0,0 @@
-#!/bin/bash
-#
-#SBATCH --job-name=s4_2
-#SBATCH --output=s4_2.txt
-#
-#number of CPUs to be used
-#SBATCH --ntasks=1
-#SBATCH -c 24
-#
-#Define the number of hours the job should run.
-#Maximum runtime is limited to 10 days, ie. 240 hours
-#SBATCH --time=120:00:00
-#
-#Define the amount of system RAM used by your job in GigaBytes
-#SBATCH --mem=500G
-
-#SBATCH --partition=gpu
-#SBATCH --gres=gpu:4
-#SBATCH --constraint=A10
-
-#Send emails when a job starts, it is finished or it exits
-#SBATCH --mail-user=mlechner@ist.ac.at
-#SBATCH --mail-type=END,FAIL
-#
-#SBATCH --no-requeue
-#SBATCH --export=NONE
-unset SLURM_EXPORT_ENV
-
-
-module load python/3.8.12
-module load cuda/11.2.2
-module load cudnn/8.2.4.15
-#module load cudnn/8.1.0.77
-#module load cudnn/8.1.1.33
-
-
-
-cd $HOME/s4
-source venv/bin/activate
-
-#  lr: 0.0005
-#  weight_decay: 0.05
-
-# trainer.gpus=2
-python -m train wandb=null experiment=s4-lra-pathx-new trainer.gpus=4 optimizer.lr=0.0008
-python -m train wandb=null experiment=s4-lra-pathx-new trainer.gpus=4 optimizer.weight_decay=0.02
\ No newline at end of file