diff --git a/src/levanter/main/sft.py b/src/levanter/main/sft.py
index 4bcae2ce0..f63b8a4e9 100644
--- a/src/levanter/main/sft.py
+++ b/src/levanter/main/sft.py
@@ -186,7 +186,6 @@ def train(config: SFTConfig):
 
         # some axes we need
         Pos = config.model.Pos
-
         # to do partitioning, our dimensions have to be divisible by the size of the physical axes they're mapped to
         # For most things, we just insist you specify the config right, but tokenizers often have strange numbers of
         # tokens: gpt-2 has 50257, for example. So we round up.
@@ -210,12 +209,12 @@ def train(config: SFTConfig):
             callbacks.log_performance_stats(Pos.size, trainer.config.train_batch_size, flops_per_example), every=1
         )
 
-        # reshuffle the examples before packing!
+        # TODO: reshuffle the examples before packing!
+        # Get current step from trainer state
+        current_step = int(state.step)
 
-        # to implement seeking
-        # check the step number in the trainer state if it's not zero
-        # then next the iterator until we get there, then continue training.
-        # batch size will be backed in from config 
+        
+       
 
         # change iterate tokenized requests to take a dict rather than a list
         # of where the first element is prompt ands econd is response
@@ -227,6 +226,22 @@ def train(config: SFTConfig):
         logger.info("Creating prompt completion iterator")
         prompt_completion_iterator = create_prompt_completion_iterator(train_dataset, Pos)
 
+        if current_step > 0:
+            logger.info(f"Resuming training from step {current_step}")
+            # Calculate how many examples to skip based on batch size
+            examples_to_skip = current_step * trainer.config.train_batch_size
+            
+            # Skip through the iterator until we reach the right position
+            for _ in range(examples_to_skip):
+                try:
+                    next(prompt_completion_iterator)
+                except StopIteration:
+                    logger.warning("Ran out of examples while seeking - restarting from beginning")
+                    # Recreate iterator and continue skipping
+                    prompt_completion_iterator = create_prompt_completion_iterator(train_dataset, Pos)
+        else:
+            logger.info("Starting SFT from scratch")
+
         logger.info("Packing prompt completions")
         packed_iterator = _pack_requests(prompt_completion_iterator, tokenizer, Pos, max_pack_size=4)
         logger.info("Stacking batches to train batch")
@@ -279,20 +294,21 @@ def create_prompt_completion_iterator(cached_dataset: AsyncDataset, Pos: hax.Axi
     for i in range(length):
         example = asyncio.run(cached_dataset.getitem_async(i))
         
-        if int(example["sources_len"]) > Pos.size - 1:
+        sources_len = example["sources_len"].item()
+        if sources_len > Pos.size - 1:
             continue
             
         ids = example["input_ids"].tolist()
         if len(ids) > Pos.size:
             ids = ids[:Pos.size]
             
-        if len(ids) <= example["sources_len"]:
+        if len(ids) <= sources_len:
             continue
             
         try:
             yield PromptCompletion(
                 ids=ids,
-                prompt_length=int(example["sources_len"]),
+                prompt_length=sources_len,
                 segment_id=i
             )
         except ValueError: