diff --git a/scripts/ablate_arch/gemma_xl_per_rank_gen_fac2.sh b/scripts/ablate_arch/gemma_xl_per_rank_gen_fac2.sh
index 0ab70bf..93df9d7 100644
--- a/scripts/ablate_arch/gemma_xl_per_rank_gen_fac2.sh
+++ b/scripts/ablate_arch/gemma_xl_per_rank_gen_fac2.sh
@@ -2,6 +2,7 @@
 #SBATCH --job-name=ctxlora
 #SBATCH --partition=a3
 #SBATCH --nodes=1
+#SBATCH --exclude=slurm0-a3nodeset-2
 #SBATCH --gpus=4
 #SBATCH --output=outputs/%x-%j.out
 #SBATCH --error=outputs/%x-%j.out
diff --git a/scripts/ablate_arch/gemma_xl_per_rank_gen_fac2_per_layer_processing.sh b/scripts/ablate_arch/gemma_xl_per_rank_gen_fac2_per_layer_processing.sh
new file mode 100644
index 0000000..935c406
--- /dev/null
+++ b/scripts/ablate_arch/gemma_xl_per_rank_gen_fac2_per_layer_processing.sh
@@ -0,0 +1,34 @@
+#!/bin/bash
+#SBATCH --job-name=ctxlora
+#SBATCH --partition=a3
+#SBATCH --nodes=1
+#SBATCH --exclude=slurm0-a3nodeset-2
+#SBATCH --gpus=4
+#SBATCH --output=outputs/%x-%j.out
+#SBATCH --error=outputs/%x-%j.out
+
+# module load
+# module load cuda/12.1
+# module load cudnn/8.9.7
+# module load nccl/cuda-12.1/2.18.3
+# module load hpcx/2.20
+
+# export OMP_NUM_THREADS=24
+# export TRITON_CACHE_DIR=/tmp/.triton/
+. ~/miniconda3/etc/profile.d/conda.sh
+conda activate /home/rujikorn_sakana_ai/.conda/envs/ctx-to-lora
+# eval "$@"
+
+accelerate launch --num_processes=4 --gradient_accumulation_steps=8 --gradient_clipping=1.0 \
+--gpu_ids all --main_process_port 29562 intx_sft.py configs/pretrain_all_xl.yaml \
+--model_name_or_path=google/gemma-2-2b-it --num_train_epochs=5.1 --per_device_train_batch_size=32 \
+--gradient_accumulation_steps=8 --per_device_eval_batch_size=32 --exp_setup=hyper_lora --aggregator_type=perceiver \
+--target_modules=down_proj \
+--num_self_attends_per_block=8 --num_latent_factor=2 \
+--lora_r=8 \
+--eval_steps=5000 --save_steps=5000 --learning_rate=2e-5 --lora_dropout=0.0 \
+--neftune_noise_alpha=5 --use_light_weight_lora=False \
+--load_best_model_at_end=True --metric_for_best_model=pwc_loss --add_negative_prompt=False \
+--add_repeat_prompt=False \
+--use_sequence_packing=True --per_rank_gen=True \
+--per_layer_processing=True
diff --git a/src/ctx_to_lora/modeling_utils.py b/src/ctx_to_lora/modeling_utils.py
index c1c4f23..3ccd908 100644
--- a/src/ctx_to_lora/modeling_utils.py
+++ b/src/ctx_to_lora/modeling_utils.py
@@ -756,14 +756,14 @@ class HyperLoRA(nn.Module):
             if self.config.per_layer_processing:
                 layers = [
                     # nn.Linear(self.d_latent, self.d_latent),
-                    # Mix(
-                    #     "bs n_layers n_modules r d -> bs n_layers n_modules r d0",
-                    #     weight_shape="n_layers d d0",
-                    #     bias_shape="n_layers d0",
-                    #     n_layers=self.n_layers,
-                    #     d=self.d_latent,
-                    #     d0=self.d_latent,
-                    # ),
+                    Mix(
+                        "bs n_layers n_modules r d -> bs n_layers n_modules r d0",
+                        weight_shape="n_layers d d0",
+                        bias_shape="n_layers d0",
+                        n_layers=self.n_layers,
+                        d=self.d_latent,
+                        d0=self.d_latent,
+                    ),
                     ResMLPBlockPerLayer(
                         self.n_layers,
                         self.d_latent,
@@ -776,6 +776,7 @@ class HyperLoRA(nn.Module):
                 ]
             else:
                 layers = [
+                    nn.Linear(self.d_latent, self.d_latent),
                     MLPResidualBlock(
                         input_size=self.config.latent_size,
                         hidden_size=self.config.latent_size * 4,