diff --git a/extra/onnx.py b/extra/onnx.py
index c1d5bf0163..0d809440d2 100644
--- a/extra/onnx.py
+++ b/extra/onnx.py
@@ -2,8 +2,8 @@ from __future__ import annotations
 from google.protobuf.internal.containers import RepeatedCompositeFieldContainer
 import importlib
 import numpy as np
-from tinygrad import Tensor, dtypes
-from tinygrad.helpers import getenv, DEBUG
+from tinygrad import Tensor, dtypes, Device
+from tinygrad.helpers import getenv, DEBUG, CI, OSX
 from typing import List, Dict
 from onnx import AttributeProto, ModelProto, TensorProto, TypeProto # onnx 1.50 uses serialized file (see onnx/onnx-ml.proto) as descriptors
 try:
@@ -24,6 +24,14 @@ def safe_numpy(t) -> np.ndarray:
     numpy_cache[t] = tmp
   return numpy_cache[t]
 
+# copied from helpers.py
+def is_dtype_supported(dtype, device: str = Device.DEFAULT):
+  if dtype == dtypes.bfloat16: return False
+  if device in ["WEBGPU", "WEBGL"]: return dtype in [dtypes.float, dtypes.int32, dtypes.uint32]
+  if dtype == dtypes.half: return not (CI and device in {"GPU", "LLVM", "CUDA"})
+  if dtype == dtypes.float64: return device != "METAL" and not (OSX and device == "GPU")
+  return True
+
 # src: onnx/mapping.py
 # not supported: STRING = 8 COMPLEX64 = 14, COMPLEX128 = 15
 # NOTE: 17, 18, 19, 20 are float8, 10 is half
@@ -58,19 +66,13 @@ def get_run_onnx(onnx_model: ModelProto):
       else: raise Exception(f"unknown attr: {attr}, {type_proto}")
 
   def buffer_parse(inp: TensorProto) -> Tensor:
-    if inp.data_type in (1,10,6,7,5,11):
-      # TODO: this is shared with below
-      if len(inp.float_data) > 0:
-        ret = Tensor(np.array(inp.float_data, dtype=np.float32).reshape(inp.dims), requires_grad=False)
-      elif len(inp.int64_data) > 0:
-        ret = Tensor(np.array(inp.int64_data, dtype=np.int64).reshape(inp.dims), requires_grad=False)
-      elif len(inp.int32_data) > 0:
-        ret = Tensor(np.array(inp.int32_data, dtype=np.int32).reshape(inp.dims), requires_grad=False)
-      else:
-        ret = Tensor(np.frombuffer(inp.raw_data, dtype=tensor_dtype_to_np_dtype(inp.data_type)).reshape(inp.dims).astype(np.float32).copy(), requires_grad=False)
-    else:
-      raise Exception(f"bad data type {inp.name} {inp.dims} {inp.data_type}")
-    return ret
+    if inp.data_type in (8,14,15): raise Exception(f"data type not supported {inp.name} {inp.dims} {inp.data_type}")
+    dtype = DTYPE_MAP[inp.data_type] if is_dtype_supported(DTYPE_MAP[inp.data_type]) else dtypes.float32
+    if dat := list(inp.float_data) or list(inp.int32_data) or list(inp.int64_data):
+      return Tensor(dat, dtype=dtype, requires_grad=False).reshape(tuple(inp.dims))
+    if len(inp.raw_data) > 0:
+      return Tensor(np.frombuffer(inp.raw_data, dtype=tensor_dtype_to_np_dtype(inp.data_type)).astype(dtype.np).copy(), requires_grad=False).reshape(tuple(inp.dims))
+    return Tensor(None, requires_grad=False)
 
   def attribute_parse(a: AttributeProto) -> float | int | str | Tensor | tuple[float] | tuple[int]:
     # TODO: this is not complete, see onnx/onnx_ml_pb2.pyi for a complete list
@@ -89,18 +91,7 @@ def get_run_onnx(onnx_model: ModelProto):
 
   # get weights and biases
   for inp in onnx_model.graph.initializer:
-    if len(inp.raw_data) > 0:
-      tensors[inp.name] = buffer_parse(inp)
-    elif len(inp.float_data) > 0:
-      tensors[inp.name] = Tensor(np.array(inp.float_data, dtype=np.float32).reshape(inp.dims), requires_grad=False)
-    elif len(inp.int64_data) > 0:
-      tensors[inp.name] = Tensor(np.array(inp.int64_data, dtype=np.int64).reshape(inp.dims), requires_grad=False)
-    elif len(inp.raw_data) == 0:
-      tensors[inp.name] = Tensor(np.array([], dtype=np.float32), requires_grad=False)
-    else:
-      print(inp.name, inp.dims, inp.data_type, len(inp.raw_data))
-      print(inp)
-      raise Exception("no data")
+    tensors[inp.name] = buffer_parse(inp)
 
   # preparse the attributes
   attribute_dict = {}