diff --git a/sae_lens/training/lm_runner.py b/sae_lens/training/lm_runner.py
index a3c2d673..eb20f598 100644
--- a/sae_lens/training/lm_runner.py
+++ b/sae_lens/training/lm_runner.py
@@ -14,6 +14,9 @@
 
 def language_model_sae_runner(cfg: LanguageModelSAERunnerConfig):
     """ """
+    training_run_state = None
+    train_contexts = None
+
     if cfg.resume:
         try:
             checkpoint_path = cfg.get_resume_checkpoint_path()
@@ -41,8 +44,6 @@ def language_model_sae_runner(cfg: LanguageModelSAERunnerConfig):
             cfg.resume = False
 
     if not cfg.resume:
-        training_run_state = None
-        train_contexts = None
         if cfg.from_pretrained_path is not None:
             (
                 model,
@@ -62,11 +63,10 @@ def language_model_sae_runner(cfg: LanguageModelSAERunnerConfig):
         wandb.init(project=cfg.wandb_project, config=cast(Any, cfg), name=cfg.run_name)
 
     # train SAE
-
     sparse_autoencoder = train_sae_group_on_language_model(
-        model=model,
-        sae_group=sparse_autoencoder,
-        activation_store=activations_loader,
+        model=model,  # pyright: ignore [reportPossiblyUnboundVariable]
+        sae_group=sparse_autoencoder,  # pyright: ignore [reportPossiblyUnboundVariable]
+        activation_store=activations_loader,  # pyright: ignore [reportPossiblyUnboundVariable]
         train_contexts=train_contexts,
         training_run_state=training_run_state,
         batch_size=cfg.train_batch_size,
diff --git a/sae_lens/training/train_sae_on_language_model.py b/sae_lens/training/train_sae_on_language_model.py
index a42f2f13..ff31cec9 100644
--- a/sae_lens/training/train_sae_on_language_model.py
+++ b/sae_lens/training/train_sae_on_language_model.py
@@ -104,8 +104,8 @@ def load(cls, path: str, sae: SparseAutoencoder, total_training_steps: int):
                 state_dict[attr.name] = value
             # non-tensor values (like int or bool)
             elif not type(value) is torch.Tensor:
-                state_dict[attr.name] = state_dict[attr.name].item()
-        ctx = cls(**state_dict)
+                state_dict[attr.name] = state_dict[attr.name].item()  # pyright: ignore [reportArgumentType]
+        ctx = cls(**state_dict)  # pyright: ignore [reportArgumentType]
         # if fine tuning, we need to set sae requires grad properly
         if ctx.finetuning:
             ctx.begin_finetuning(sae=sae)
@@ -129,9 +129,9 @@ class SAETrainingRunState:
     n_training_tokens: int = 0
     started_fine_tuning: bool = False
     checkpoint_paths: list[str] = field(default_factory=list)
-    torch_state: Optional[torch.ByteTensor] = None
-    torch_cuda_state: Optional[torch.ByteTensor] = None
-    numpy_state: Optional[tuple[str, np.ndarray[np.uint32], int, int, float]] = None
+    torch_state: Optional[torch.Tensor] = None
+    torch_cuda_state: Optional[list[torch.Tensor]] = None
+    numpy_state: Optional[dict[str, Any] | tuple[str, np.ndarray[Any, np.dtype[np.uint32]], int, int, float]] = None
     random_state: Optional[Any] = None
 
     def __post_init__(self):
@@ -145,9 +145,13 @@ def __post_init__(self):
             self.random_state = random.getstate()
 
     def set_random_state(self):
+        assert self.torch_state is not None
         torch.random.set_rng_state(self.torch_state)
+        assert self.torch_cuda_state is not None
         torch.cuda.set_rng_state_all(self.torch_cuda_state)
+        assert self.numpy_state is not None
         np.random.set_state(self.numpy_state)
+        assert self.random_state is not None
         random.setstate(self.random_state)
 
     @classmethod