diff --git a/onnx_diagnostic/torch_models/hghub/hub_data_cached_configs.py b/onnx_diagnostic/torch_models/hghub/hub_data_cached_configs.py
index ff6cdb5e..55789b8c 100644
--- a/onnx_diagnostic/torch_models/hghub/hub_data_cached_configs.py
+++ b/onnx_diagnostic/torch_models/hghub/hub_data_cached_configs.py
@@ -4687,3 +4687,145 @@ def _ccached_zai_glm_45():
             },
         }
     )
+
+
+def _ccached_microsoft_phi3_mini_128k_instruct():
+    "microsoft/Phi-3-mini-128k-instruct"
+    return transformers.Phi3Config(
+        **{
+            "_name_or_path": "Phi-3-mini-128k-instruct",
+            "architectures": ["Phi3ForCausalLM"],
+            "attention_dropout": 0.0,
+            "auto_map": {
+                "AutoConfig": "configuration_phi3.Phi3Config",
+                "AutoModelForCausalLM": "modeling_phi3.Phi3ForCausalLM",
+            },
+            "bos_token_id": 1,
+            "embd_pdrop": 0.0,
+            "eos_token_id": 32000,
+            "hidden_act": "silu",
+            "hidden_size": 3072,
+            "initializer_range": 0.02,
+            "intermediate_size": 8192,
+            "max_position_embeddings": 131072,
+            "model_type": "phi3",
+            "num_attention_heads": 32,
+            "num_hidden_layers": 32,
+            "num_key_value_heads": 32,
+            "original_max_position_embeddings": 4096,
+            "pad_token_id": 32000,
+            "resid_pdrop": 0.0,
+            "rms_norm_eps": 1e-05,
+            "rope_scaling": {
+                "long_factor": [
+                    1.0700000524520874,
+                    1.1200000047683716,
+                    1.149999976158142,
+                    1.4199999570846558,
+                    1.5699999332427979,
+                    1.7999999523162842,
+                    2.129999876022339,
+                    2.129999876022339,
+                    3.009999990463257,
+                    5.910000324249268,
+                    6.950000286102295,
+                    9.070000648498535,
+                    9.930000305175781,
+                    10.710000038146973,
+                    11.130000114440918,
+                    14.609999656677246,
+                    15.409998893737793,
+                    19.809999465942383,
+                    37.279998779296875,
+                    38.279998779296875,
+                    38.599998474121094,
+                    40.12000274658203,
+                    46.20000457763672,
+                    50.940006256103516,
+                    53.66000747680664,
+                    54.9373893737793,
+                    56.89738845825195,
+                    57.28738784790039,
+                    59.98738479614258,
+                    60.86738586425781,
+                    60.887386322021484,
+                    61.71739196777344,
+                    62.91739273071289,
+                    62.957393646240234,
+                    63.41739273071289,
+                    63.8173942565918,
+                    63.83739471435547,
+                    63.897396087646484,
+                    63.93739700317383,
+                    64.06739807128906,
+                    64.11434936523438,
+                    64.12435150146484,
+                    64.15435028076172,
+                    64.19435119628906,
+                    64.24435424804688,
+                    64.57435607910156,
+                    64.69000244140625,
+                    64.76000213623047,
+                ],
+                "short_factor": [
+                    1.1,
+                    1.1,
+                    1.1,
+                    1.3000000000000003,
+                    1.3500000000000003,
+                    1.3500000000000003,
+                    1.4000000000000004,
+                    1.5500000000000005,
+                    2.000000000000001,
+                    2.000000000000001,
+                    2.000000000000001,
+                    2.000000000000001,
+                    2.000000000000001,
+                    2.000000000000001,
+                    2.000000000000001,
+                    2.000000000000001,
+                    2.000000000000001,
+                    2.000000000000001,
+                    2.000000000000001,
+                    2.000000000000001,
+                    2.000000000000001,
+                    2.000000000000001,
+                    2.000000000000001,
+                    2.000000000000001,
+                    2.000000000000001,
+                    2.0500000000000007,
+                    2.0500000000000007,
+                    2.0500000000000007,
+                    2.0500000000000007,
+                    2.0500000000000007,
+                    2.0500000000000007,
+                    2.1000000000000005,
+                    2.1000000000000005,
+                    2.1500000000000004,
+                    2.25,
+                    2.25,
+                    2.25,
+                    2.25,
+                    2.25,
+                    2.3999999999999995,
+                    2.4499999999999993,
+                    2.499999999999999,
+                    2.6999999999999984,
+                    2.6999999999999984,
+                    2.7499999999999982,
+                    2.799999999999998,
+                    2.8999999999999977,
+                    3.049999999999997,
+                ],
+                "type": "longrope",
+            },
+            "rope_theta": 10000.0,
+            "sliding_window": 262144,
+            "tie_word_embeddings": false,
+            "torch_dtype": "bfloat16",
+            "transformers_version": "4.40.2",
+            "use_cache": true,
+            "attention_bias": false,
+            "vocab_size": 32064,
+        }
+    )
diff --git a/onnx_diagnostic/torch_models/validate.py b/onnx_diagnostic/torch_models/validate.py
index 1579bbcb..f64673f3 100644
--- a/onnx_diagnostic/torch_models/validate.py
+++ b/onnx_diagnostic/torch_models/validate.py
@@ -712,6 +712,7 @@ def validate_model(
                 print(f"[validate_model] done (dump onnx) in {duration}")
             data["onnx_filename"] = onnx_filename
             summary["time_onnx_save"] = duration
+            summary.update(compute_statistics(onnx_filename))
         if verbose:
             print(f"[validate_model] dumps statistics in {dump_folder!r}...")
         dump_stats = os.path.join(dump_folder, f"{folder_name}.stats")
@@ -815,6 +816,39 @@ def validate_model(
     return summary, data
 
 
+def compute_statistics(onnx_filename: str) -> Dict[str, Union[float, int]]:
+    """Computes some statistics on the model itself."""
+    onx = onnx.load(onnx_filename, load_external_data=False)
+
+    def node_iter(proto):
+        if isinstance(proto, onnx.ModelProto):
+            yield from node_iter(proto.graph)
+            for f in proto.functions:
+                yield from node_iter(f)
+        elif isinstance(proto, (onnx.FunctionProto, onnx.GraphProto)):
+            for node in proto.node:
+                yield node
+                for att in node.attribute:
+                    if att.type == onnx.AttributeProto.GRAPH:
+                        yield from node_iter(att.g)
+            if hasattr(proto, "initializer"):
+                yield from proto.initializer
+        else:
+            raise NotImplementedError(f"Unexpected type={type(proto)}")
+
+    counts: Dict[str, Union[float, int]] = {}
+    for proto in node_iter(onx):
+        if isinstance(proto, onnx.NodeProto):
+            key = f"n_node_{proto.op_type}"
+        else:
+            key = f"n_node_initializer_{proto.data_type}"
+
+        if key not in counts:
+            counts[key] = 0
+        counts[key] += 1
+    return counts
+
+
 def _validate_do_run_model(
     data, summary, key, tag, expected_tag, verbose, repeat, warmup, quiet
 ):