diff --git a/candle-onnx/src/eval.rs b/candle-onnx/src/eval.rs
index a1128c54f3..eedf3e9945 100644
--- a/candle-onnx/src/eval.rs
+++ b/candle-onnx/src/eval.rs
@@ -358,6 +358,12 @@ fn simple_eval_(
                 let output = input0.broadcast_div(input1)?;
                 values.insert(node.output[0].clone(), output);
             }
+            "Reciprocal" => {
+                let xs = get(&node.input[0])?;
+                let ones = Tensor::ones_like(&xs)?;
+                let output = ones.div(xs)?;
+                values.insert(node.output[0].clone(), output);
+            }
             "Pow" => {
                 let input0 = get(&node.input[0])?;
                 let input1 = get(&node.input[1])?;
@@ -488,6 +494,29 @@ fn simple_eval_(
                 };
                 values.insert(node.output[0].clone(), ys);
             }
+            "GlobalAveragePool" => {
+                // https://github.com/onnx/onnx/blob/main/docs/Operators.md#GlobalAveragePool
+                let xs = get(&node.input[0])?;
+                let [_n_dim, _c_dim, kernel_shape @ ..] = xs.dims() else {
+                    bail!(
+                        "only 2d GlobalAveragePool is supported, kernel shape {:?}",
+                        xs.dims()
+                    );
+                };
+                let ys = match kernel_shape {
+                    [d1, d2] => xs.avg_pool2d((*d1, *d2)),
+                    [d1] => {
+                        let xs = xs.unsqueeze(1)?;
+                        let xs = xs.avg_pool2d((1, *d1))?;
+                        xs.squeeze(1)
+                    }
+                    _ => bail!(
+                        "only 2d GlobalAveragePool is supported, kernel shape {:?}",
+                        xs.dims()
+                    ),
+                }?;
+                values.insert(node.output[0].clone(), ys);
+            }
             "AveragePool" => {
                 // https://github.com/onnx/onnx/blob/main/docs/Operators.md#AveragePool
                 let dilations = get_attr_opt::<[i64]>(node, "dilations")?;
@@ -1121,13 +1150,6 @@ fn simple_eval_(
                 let mode = get_attr_opt(node, "mode")?.unwrap_or("constant");
                 let data = get(&node.input[0])?;
                 let pads = get(&node.input[1])?;
-                if node.input.len() > 2 {
-                    bail!(
-                        "unsupported number of inputs {} for Pad node {:?}, expected 2",
-                        node.input.len(),
-                        node.name
-                    );
-                }
                 if pads.rank() != 1 {
                     bail!("Pad expects 'pads' input to be 1D vector: {pads:?}");
                 }
@@ -1164,6 +1186,34 @@ fn simple_eval_(
 
                         values.insert(node.output[0].clone(), out);
                     }
+                    "constant" => {
+                        let value = if node.input.len() > 2 {
+                            get(&node.input[2])?.to_vec0::<f32>()?
+                        } else {
+                            0.0
+                        };
+
+                        let mut out = data.clone();
+                        for (axis, (pad_pre, pad_post)) in
+                            pads_pre.iter().zip(pads_post).enumerate()
+                        {
+                            if *pad_pre == 0 && *pad_post == 0 {
+                                continue;
+                            }
+
+                            let mut new_dims = out.dims().to_vec();
+                            new_dims[axis] += (*pad_pre + *pad_post) as usize;
+
+                            out = Tensor::full(value, new_dims, out.device())?.slice_scatter(
+                                &out,
+                                axis,
+                                *pad_pre as usize,
+                            )?;
+                        }
+
+                        values.insert(node.output[0].clone(), out);
+                    }
+
                     _ => bail!(
                         "unsupported 'mode' value {mode:?} for Pad node {:?}",
                         node.name