diff --git a/hivemind/compression/floating.py b/hivemind/compression/floating.py
index ee0c62898..73c37522a 100644
--- a/hivemind/compression/floating.py
+++ b/hivemind/compression/floating.py
@@ -12,7 +12,8 @@ class Float16Compression(CompressionBase):
     FP16_MIN, FP16_MAX = torch.finfo(torch.float16).min, torch.finfo(torch.float16).max
 
     def compress(self, tensor: torch.Tensor, info: CompressionInfo, allow_inplace: bool = False) -> runtime_pb2.Tensor:
-        assert torch.is_floating_point(tensor) and tensor.dtype != torch.bfloat16
+        if not torch.is_floating_point(tensor) or tensor.dtype == torch.bfloat16:
+            raise ValueError(f"{self.__class__.__name__} does not support {tensor.dtype} tensors")
         requires_grad = tensor.requires_grad
         tensor = tensor.detach().cpu()
         dtype_name = tensor.numpy().dtype.name
@@ -47,7 +48,8 @@ class ScaledFloat16Compression(Float16Compression):
     FP32_EPS = torch.finfo(torch.float32).eps
 
     def compress(self, tensor: torch.Tensor, info: CompressionInfo, allow_inplace: bool = False) -> runtime_pb2.Tensor:
-        assert torch.is_floating_point(tensor) and tensor.dtype != torch.bfloat16
+        if not torch.is_floating_point(tensor) or tensor.dtype == torch.bfloat16:
+            raise ValueError(f"{self.__class__.__name__} does not support {tensor.dtype} tensors")
         requires_grad = tensor.requires_grad
         tensor = tensor.detach().cpu()
         dtype_name = tensor.numpy().dtype.name
diff --git a/hivemind/compression/quantization.py b/hivemind/compression/quantization.py
index acb3bc804..257d09bca 100644
--- a/hivemind/compression/quantization.py
+++ b/hivemind/compression/quantization.py
@@ -25,7 +25,8 @@ def quantize(self, tensor: torch.Tensor, allow_inplace: bool = False) -> Tuple[n
         ...
 
     def compress(self, tensor: torch.Tensor, info: CompressionInfo, allow_inplace: bool = False) -> runtime_pb2.Tensor:
-        assert torch.is_floating_point(tensor)
+        if not torch.is_floating_point(tensor):
+            raise ValueError(f"{self.__class__.__name__} does not support {tensor.dtype} tensors")
         quantized, codebook = self.quantize(tensor.detach(), allow_inplace=allow_inplace)
         return runtime_pb2.Tensor(
             compression=self.compression_type,
@@ -128,6 +129,7 @@ def quantile_qq_approximation(array: np.ndarray, n_quantiles: int, min_chunk_siz
 class BlockwiseQuantization(Quantization):
     compression_type = runtime_pb2.BLOCKWISE_8BIT
     codebook_dtype, indices_dtype = np.float32, np.uint8
+    EXTRA_PARAMS = (4096, False, torch.float32, None, None)
 
     def quantize(
         self, tensor: torch.Tensor, allow_inplace: bool = False
@@ -139,7 +141,7 @@ def quantize(
             raise ImportError(BNB_MISSING_MESSAGE)
 
         quantized, (absmax, codebook, *extra_params) = quantize_blockwise(tensor, blocksize=4096, nested=False)
-        assert tuple(extra_params) == (4096, False, tensor.dtype, None, None)  # blocksize, nested, dtype, offset, s2
+        assert tuple(extra_params) == self.EXTRA_PARAMS  # blocksize, nested, dtype, offset, state2
         return quantized.numpy(), (absmax.numpy(), codebook.numpy())
 
     def compress(self, tensor: torch.Tensor, info: CompressionInfo, allow_inplace: bool = False) -> runtime_pb2.Tensor:
@@ -185,5 +187,5 @@ def extract(self, serialized_tensor: runtime_pb2.Tensor) -> torch.Tensor:
         absmax = torch.as_tensor(absmax)
         codebook = torch.as_tensor(codebook)
         quantized = torch.as_tensor(quantized).reshape(tuple(serialized_tensor.size))
-        result = dequantize_blockwise(quantized, (absmax, codebook, 4096, False, torch.float32, None, None))
+        result = dequantize_blockwise(quantized, (absmax, codebook, *self.EXTRA_PARAMS))
         return result.to(getattr(torch, serialized_tensor.dtype)).requires_grad_(serialized_tensor.requires_grad)