diff --git a/src/axolotl/core/trainers/base.py b/src/axolotl/core/trainers/base.py
index 3864903a5..ab9735adc 100644
--- a/src/axolotl/core/trainers/base.py
+++ b/src/axolotl/core/trainers/base.py
@@ -114,6 +114,8 @@ class AxolotlTrainer(
             packing_efficiency_estimate=self.args.sample_packing_efficiency,
             batch_max_len=batch_max_len,
             batch_size=batch_size,
+            group_size=self.args.sample_packing_group_size,
+            bin_size=self.args.sample_packing_bin_size,
             sequential=self.args.sample_packing_sequentially,
             drop_last=True,
         )
diff --git a/src/axolotl/utils/samplers/multipack.py b/src/axolotl/utils/samplers/multipack.py
index 1abe594e3..394470b4b 100644
--- a/src/axolotl/utils/samplers/multipack.py
+++ b/src/axolotl/utils/samplers/multipack.py
@@ -61,7 +61,8 @@ def pack_group(
     group_offset: int,
     bin_capacity: int,
     max_bins: int,
-    safe_mode: bool = False,
+    bin_size: int,
+    safe_mode: bool = True,
 ):
     """
     Pack a group of sequences into bins using First-Fit Decreasing algorithm
@@ -71,6 +72,7 @@ def pack_group(
         group_offset: Offset to apply to indices when returning results
         bin_capacity: Maximum capacity of each bin
         max_bins: Maximum number of bins to use
+        bin_size: Maximum number of sequences per bin
         safe_mode: If True, use a more conservative packing approach
 
     Returns:
@@ -89,7 +91,10 @@ def pack_group(
         # Try to place sequence in existing bins
         add_new_bin = True
         for bin_idx, _ in enumerate(bins_remaining_space):
-            if bins_remaining_space[bin_idx] >= size:
+            if (
+                bins_remaining_space[bin_idx] >= size
+                and len(bins_assigned_sequences[bin_idx]) < bin_size
+            ):
                 bins_remaining_space[bin_idx] -= size
                 bins_assigned_sequences[bin_idx].append(global_idx)
                 add_new_bin = False
@@ -112,14 +117,17 @@ def pack_group(
 
 # Define a standalone function for multiprocessing
 def _process_group(args):
-    group_lengths, start_idx, bin_capacity, max_bins, safe_mode = args
-    return pack_group(group_lengths, start_idx, bin_capacity, max_bins, safe_mode)
+    group_lengths, start_idx, bin_capacity, max_bins, bin_size, safe_mode = args
+    return pack_group(
+        group_lengths, start_idx, bin_capacity, max_bins, bin_size, safe_mode
+    )
 
 
 def pack_parallel(
     sequence_lengths: np.ndarray,
     bin_capacity: int,
     group_size: int,
+    bin_size: int,
     num_processes: int | None = None,
     safe_mode: bool = True,
 ):
@@ -128,8 +136,9 @@ def pack_parallel(
 
     Args:
         sequence_lengths: Array of sequence lengths
-        bin_capacity: Maximum capacity of each bin
+        bin_capacity: Maximum capacity of each bin as total number of tokens
         group_size: Number of sequences to process in each group
+        bin_size: Maximum number of bins to use
         num_processes: Number of parallel processes to use
         safe_mode: If True, use a more conservative packing approach
 
@@ -145,7 +154,7 @@ def pack_parallel(
     for i in range(0, num_items, group_size):
         group_lengths = sequence_lengths[i : i + group_size]
         max_bins = len(group_lengths)  # Allow as many bins as items in the group
-        tasks.append((group_lengths, i, bin_capacity, max_bins, safe_mode))
+        tasks.append((group_lengths, i, bin_capacity, max_bins, bin_size, safe_mode))
 
     # Process groups in parallel
     all_bins = []
@@ -230,6 +239,7 @@ class MultipackBatchSampler(BatchSampler):
         num_count_samples: int = 16,  # Number of samples to estimate batch count
         sequential: bool = False,  # Whether to use sequential packing
         group_size: int = 100_000,  # Size of groups for parallel packing
+        bin_size: int = 200,  # The max number of samples that can be packed in a single bin
         num_processes: int | None = None,  # Number of processes for parallel packing
         safe_mode: bool = True,  # Conservative packing to prevent training instability
         **kwargs,  # pylint: disable=unused-argument
@@ -241,6 +251,7 @@ class MultipackBatchSampler(BatchSampler):
         self.packing_efficiency_estimate = packing_efficiency_estimate or 1.0
         self.sequential = sequential
         self.group_size = group_size
+        self.bin_size = bin_size
         self.num_processes = num_processes
         self.safe_mode = safe_mode
 
@@ -261,7 +272,7 @@ class MultipackBatchSampler(BatchSampler):
         self._batches = None
 
         if self.sequential and not isinstance(sampler, SequentialSampler):
-            LOG.warn(
+            LOG.warning(
                 "using sequential sample packing with non-sequential sampler, did you want to also enable curriculum_sampling?"
             )
 
@@ -306,6 +317,7 @@ class MultipackBatchSampler(BatchSampler):
                 lengths,
                 bin_capacity=self.batch_max_len,
                 group_size=self.group_size,
+                bin_size=self.bin_size,
                 num_processes=self.num_processes,
                 safe_mode=self.safe_mode,
             )