update with major refactor

fold0/config.yaml

@@ -5,7 +5,7 @@ trainer:
   strategy:
     class_path: lightning.pytorch.strategies.FSDPStrategy
     init_args:
-      auto_wrap_policy: [genbio_finetune.huggingface_models.fm4bio.modeling_fm4bio.FM4BioLayer]
+      auto_wrap_policy: [modelgenerator.huggingface_models.fm4bio.modeling_fm4bio.FM4BioLayer]
       sharding_strategy: HYBRID_SHARD
   devices: 4
   num_nodes: 4
@@ -118,10 +118,10 @@ trainer:
   reload_dataloaders_every_n_epochs: 0
   default_root_dir: logs
 model:
-  class_path: genbio_finetune.tasks.SequenceRegression
+  class_path: modelgenerator.tasks.SequenceRegression
   init_args:
     backbone:
-      class_path: genbio_finetune.models.proteinfm
+      class_path: modelgenerator.backbones.proteinfm
       init_args:
         from_scratch: false
         use_peft: true
@@ -133,7 +133,7 @@ model:
         model_init_args: null
         max_length: 2048
     adapter:
-      class_path: genbio_finetune.models.MLPPoolAdapter
+      class_path: modelgenerator.adapters.MLPPoolAdapter
       init_args:
         pooling: mean_pooling
         hidden_sizes:
@@ -157,13 +157,13 @@ model:
         differentiable: false
         fused: null
     lr_scheduler:
-      class_path: genbio_finetune.lr_schedulers.CosineWithWarmup
+      class_path: modelgenerator.lr_schedulers.CosineWithWarmup
       init_args:
         warmup_ratio: 0.05
     strict_loading: true
     reset_optimizer_states: false
 data:
-  class_path: genbio_finetune.data.DMSFitnessPrediction
+  class_path: modelgenerator.data.DMSFitnessPrediction
   init_args:
     path: genbio-ai/ProteinGYM-DMS
     train_split_files:

fold1/config.yaml

@@ -5,7 +5,7 @@ trainer:
   strategy:
     class_path: lightning.pytorch.strategies.FSDPStrategy
     init_args:
-      auto_wrap_policy: [genbio_finetune.huggingface_models.fm4bio.modeling_fm4bio.FM4BioLayer]
+      auto_wrap_policy: [modelgenerator.huggingface_models.fm4bio.modeling_fm4bio.FM4BioLayer]
       sharding_strategy: HYBRID_SHARD
   devices: 4
   num_nodes: 4
@@ -118,10 +118,10 @@ trainer:
   reload_dataloaders_every_n_epochs: 0
   default_root_dir: logs
 model:
-  class_path: genbio_finetune.tasks.SequenceRegression
+  class_path: modelgenerator.tasks.SequenceRegression
   init_args:
     backbone:
-      class_path: genbio_finetune.models.proteinfm
+      class_path: modelgenerator.backbones.proteinfm
       init_args:
         from_scratch: false
         use_peft: true
@@ -133,7 +133,7 @@ model:
         model_init_args: null
         max_length: 2048
     adapter:
-      class_path: genbio_finetune.models.MLPPoolAdapter
+      class_path: modelgenerator.adapters.MLPPoolAdapter
       init_args:
         pooling: mean_pooling
         hidden_sizes:
@@ -157,13 +157,13 @@ model:
         differentiable: false
         fused: null
     lr_scheduler:
-      class_path: genbio_finetune.lr_schedulers.CosineWithWarmup
+      class_path: modelgenerator.lr_schedulers.CosineWithWarmup
       init_args:
         warmup_ratio: 0.05
     strict_loading: true
     reset_optimizer_states: false
 data:
-  class_path: genbio_finetune.data.DMSFitnessPrediction
+  class_path: modelgenerator.data.DMSFitnessPrediction
   init_args:
     path: genbio-ai/ProteinGYM-DMS
     train_split_files:

fold2/config.yaml

@@ -5,7 +5,7 @@ trainer:
   strategy:
     class_path: lightning.pytorch.strategies.FSDPStrategy
     init_args:
-      auto_wrap_policy: [genbio_finetune.huggingface_models.fm4bio.modeling_fm4bio.FM4BioLayer]
+      auto_wrap_policy: [modelgenerator.huggingface_models.fm4bio.modeling_fm4bio.FM4BioLayer]
       sharding_strategy: HYBRID_SHARD
   devices: 4
   num_nodes: 4
@@ -118,10 +118,10 @@ trainer:
   reload_dataloaders_every_n_epochs: 0
   default_root_dir: logs
 model:
-  class_path: genbio_finetune.tasks.SequenceRegression
+  class_path: modelgenerator.tasks.SequenceRegression
   init_args:
     backbone:
-      class_path: genbio_finetune.models.proteinfm
+      class_path: modelgenerator.backbones.proteinfm
       init_args:
         from_scratch: false
         use_peft: true
@@ -133,7 +133,7 @@ model:
         model_init_args: null
         max_length: 2048
     adapter:
-      class_path: genbio_finetune.models.MLPPoolAdapter
+      class_path: modelgenerator.adapters.MLPPoolAdapter
       init_args:
         pooling: mean_pooling
         hidden_sizes:
@@ -157,13 +157,13 @@ model:
         differentiable: false
         fused: null
     lr_scheduler:
-      class_path: genbio_finetune.lr_schedulers.CosineWithWarmup
+      class_path: modelgenerator.lr_schedulers.CosineWithWarmup
       init_args:
         warmup_ratio: 0.05
     strict_loading: true
     reset_optimizer_states: false
 data:
-  class_path: genbio_finetune.data.DMSFitnessPrediction
+  class_path: modelgenerator.data.DMSFitnessPrediction
   init_args:
     path: genbio-ai/ProteinGYM-DMS
     train_split_files:

fold3/config.yaml

@@ -5,7 +5,7 @@ trainer:
   strategy:
     class_path: lightning.pytorch.strategies.FSDPStrategy
     init_args:
-      auto_wrap_policy: [genbio_finetune.huggingface_models.fm4bio.modeling_fm4bio.FM4BioLayer]
+      auto_wrap_policy: [modelgenerator.huggingface_models.fm4bio.modeling_fm4bio.FM4BioLayer]
       sharding_strategy: HYBRID_SHARD
   devices: 4
   num_nodes: 4
@@ -118,10 +118,10 @@ trainer:
   reload_dataloaders_every_n_epochs: 0
   default_root_dir: logs
 model:
-  class_path: genbio_finetune.tasks.SequenceRegression
+  class_path: modelgenerator.tasks.SequenceRegression
   init_args:
     backbone:
-      class_path: genbio_finetune.models.proteinfm
+      class_path: modelgenerator.backbones.proteinfm
       init_args:
         from_scratch: false
         use_peft: true
@@ -133,7 +133,7 @@ model:
         model_init_args: null
         max_length: 2048
     adapter:
-      class_path: genbio_finetune.models.MLPPoolAdapter
+      class_path: modelgenerator.adapters.MLPPoolAdapter
       init_args:
         pooling: mean_pooling
         hidden_sizes:
@@ -157,13 +157,13 @@ model:
         differentiable: false
         fused: null
     lr_scheduler:
-      class_path: genbio_finetune.lr_schedulers.CosineWithWarmup
+      class_path: modelgenerator.lr_schedulers.CosineWithWarmup
       init_args:
         warmup_ratio: 0.05
     strict_loading: true
     reset_optimizer_states: false
 data:
-  class_path: genbio_finetune.data.DMSFitnessPrediction
+  class_path: modelgenerator.data.DMSFitnessPrediction
   init_args:
     path: genbio-ai/ProteinGYM-DMS
     train_split_files:

fold4/config.yaml

@@ -5,7 +5,7 @@ trainer:
   strategy:
     class_path: lightning.pytorch.strategies.FSDPStrategy
     init_args:
-      auto_wrap_policy: [genbio_finetune.huggingface_models.fm4bio.modeling_fm4bio.FM4BioLayer]
+      auto_wrap_policy: [modelgenerator.huggingface_models.fm4bio.modeling_fm4bio.FM4BioLayer]
       sharding_strategy: HYBRID_SHARD
   devices: 4
   num_nodes: 4
@@ -118,10 +118,10 @@ trainer:
   reload_dataloaders_every_n_epochs: 0
   default_root_dir: logs
 model:
-  class_path: genbio_finetune.tasks.SequenceRegression
+  class_path: modelgenerator.tasks.SequenceRegression
   init_args:
     backbone:
-      class_path: genbio_finetune.models.proteinfm
+      class_path: modelgenerator.backbones.proteinfm
       init_args:
         from_scratch: false
         use_peft: true
@@ -133,7 +133,7 @@ model:
         model_init_args: null
         max_length: 2048
     adapter:
-      class_path: genbio_finetune.models.MLPPoolAdapter
+      class_path: modelgenerator.adapters.MLPPoolAdapter
       init_args:
         pooling: mean_pooling
         hidden_sizes:
@@ -157,13 +157,13 @@ model:
         differentiable: false
         fused: null
     lr_scheduler:
-      class_path: genbio_finetune.lr_schedulers.CosineWithWarmup
+      class_path: modelgenerator.lr_schedulers.CosineWithWarmup
       init_args:
         warmup_ratio: 0.05
     strict_loading: true
     reset_optimizer_states: false
 data:
-  class_path: genbio_finetune.data.DMSFitnessPrediction
+  class_path: modelgenerator.data.DMSFitnessPrediction
   init_args:
     path: genbio-ai/ProteinGYM-DMS
     train_split_files: