diff --git a/model/models/qwen25vl/model_vision.go b/model/models/qwen25vl/model_vision.go
index e8375c0b5..762ec614e 100644
--- a/model/models/qwen25vl/model_vision.go
+++ b/model/models/qwen25vl/model_vision.go
@@ -1,7 +1,6 @@
 package qwen25vl
 
 import (
-	"fmt"
 	"math"
 
 	"github.com/ollama/ollama/fs"
@@ -122,45 +121,32 @@ func (pe *PatchEmbedding) Forward(ctx ml.Context, pixelValues ml.Tensor, patchSi
 	shape := pixelValues.Shape()
 	numChannels := 3
 	temporalPatchSize := 2
+	numPatches := shape[1] // TODO: check this
+	// patch size from args
 	embedDim := 1280
-	numPatches := shape[1] / temporalPatchSize
 
-	// Split the input tensor into two temporal slices and process each separately
-	// First temporal slice (frame 0)
-	slice0 := pixelValues.View(ctx, 0, patchSize*patchSize*numChannels, 0, numPatches, 0, 1).Contiguous(ctx)
-	reshaped0 := slice0.Reshape(ctx,
-		patchSize,   // height
-		patchSize,   // width
-		numChannels, // channels
-		numPatches)  // batch
+	// Reshape the input tensor to match the expected dimensions
+	pixelValues = pixelValues.Reshape(ctx, patchSize*patchSize, temporalPatchSize, numChannels, numPatches)
 
-	// Second temporal slice (frame 1)
-	slice1 := pixelValues.View(ctx, 0, patchSize*patchSize*numChannels, 0, numPatches, 1, 1).Contiguous(ctx)
-	reshaped1 := slice1.Reshape(ctx,
-		patchSize,   // height
-		patchSize,   // width
-		numChannels, // channels
-		numPatches)  // batch
+	// Permute the tensor to bring the temporal dimension to the front
+	pixelValues = pixelValues.Permute(ctx, 1, 0, 2, 3).Contiguous(ctx)
 
-	// Apply the appropriate convolution to each temporal slice
-	// PatchConv0 corresponds to weights for temporal frame 0
-	// PatchConv1 corresponds to weights for temporal frame 1
-	s0, s1 := patchSize, patchSize // Use full stride as in original
+	in0 := pixelValues.View(ctx, 0, 1, pixelValues.Stride(1), pixelValues.Dim(1), pixelValues.Stride(2), pixelValues.Dim(2), pixelValues.Stride(3), pixelValues.Dim(3)).Contiguous(ctx)
+	in0 = in0.Reshape(ctx, patchSize, patchSize, numChannels, numPatches)
+	in1 := pixelValues.View(ctx, pixelValues.Stride(0), 1, pixelValues.Stride(1), pixelValues.Dim(1), pixelValues.Stride(2), pixelValues.Dim(2), pixelValues.Stride(3), pixelValues.Dim(3)).Contiguous(ctx)
+	in1 = in1.Reshape(ctx, patchSize, patchSize, numChannels, numPatches)
+
+	s0, s1 := patchSize, patchSize // Use full stride
 	p0, p1 := 0, 0                 // padding
 	d0, d1 := 1, 1                 // dilation
-
-	output0 := pe.PatchConv0.Forward(ctx, reshaped0, s0, s1, p0, p1, d0, d1)
-	output1 := pe.PatchConv1.Forward(ctx, reshaped1, s0, s1, p0, p1, d0, d1)
+	out0 := pe.PatchConv0.Forward(ctx, in0, s0, s1, p0, p1, d0, d1)
+	out1 := pe.PatchConv1.Forward(ctx, in1, s0, s1, p0, p1, d0, d1)
 
 	// Add the outputs from the two temporal convolutions
-	combined := output0.Add(ctx, output1)
+	out := out0.Add(ctx, out1)
 
-	// Reshape to required output dimensions
-	result := combined.Reshape(ctx, embedDim, numPatches)
-
-	fmt.Println(ml.Dump(ctx, result))
-
-	return result
+	// Reshape the output tensor to match the expected dimensions
+	return out.Reshape(ctx, embedDim, numPatches)
 }
 
 // VisionPatchMerger implements patch merging for the Qwen vision model