diff --git a/eng/Versions.props b/eng/Versions.props
index b8f9ec5b74..08e9fc1fe5 100644
--- a/eng/Versions.props
+++ b/eng/Versions.props
@@ -73,8 +73,8 @@
     <TorchSharpPyBridgeVersion>1.4.1</TorchSharpPyBridgeVersion>
     <AutoGenVersion>0.1.0</AutoGenVersion>
     <SemanticKernelVersion>1.15.0</SemanticKernelVersion>
-    <TorchSharpVersion>0.102.7</TorchSharpVersion>
-    <LibTorchVersion>2.2.1.1</LibTorchVersion>
+    <TorchSharpVersion>0.105.0</TorchSharpVersion>
+    <LibTorchVersion>2.5.1</LibTorchVersion>
     <!-- Build/infrastructure Dependencies -->
     <CodecovVersion>1.12.4</CodecovVersion>
     <CoverletCollectorVersion>6.0.2</CoverletCollectorVersion>
diff --git a/src/Microsoft.ML.GenAI.Core/Module/QuantizedLinear.cs b/src/Microsoft.ML.GenAI.Core/Module/QuantizedLinear.cs
index f399efe324..669625e7b8 100644
--- a/src/Microsoft.ML.GenAI.Core/Module/QuantizedLinear.cs
+++ b/src/Microsoft.ML.GenAI.Core/Module/QuantizedLinear.cs
@@ -87,9 +87,9 @@ public override Tensor forward(Tensor input)
         {
             // 8bit quantization
             using var dispose = torch.NewDisposeScope();
-            var weight = this.get_buffer("8bit_weight").to(ScalarType.Float32);
-            var zeroPoint = this.get_buffer("zeroPoint").to(ScalarType.Float32);
-            var scale = this.get_buffer("scale").to(ScalarType.Float32);
+            var weight = this.get_buffer("8bit_weight")!.to(ScalarType.Float32);
+            var zeroPoint = this.get_buffer("zeroPoint")!.to(ScalarType.Float32);
+            var scale = this.get_buffer("scale")!.to(ScalarType.Float32);
             var restoreWeight = (weight - zeroPoint.view(-1, 1)) / scale.view(-1, 1);
             // use float32
             var result = torch.matmul(input.to(ScalarType.Float32), restoreWeight.T);
@@ -106,17 +106,17 @@ public override Tensor forward(Tensor input)
         {
             using var dispose = torch.NewDisposeScope();
             var weight = this.get_buffer("4bit_weight");
-            var weightLower = weight % 16;
-            var weightUpper = weight / 16;
+            var weightLower = weight! % 16;
+            var weightUpper = weight! / 16;
             weight = torch.cat([weightUpper, weightLower], 0).to(ScalarType.Float32);
             weight = weight.view(this._outFeatures, this._inFeatures);
             weight -= 8;
             var zeroPoint = this.get_buffer("zeroPoint");
-            var zeroPointLower = zeroPoint % 16;
-            var zeroPointUpper = zeroPoint / 16;
+            var zeroPointLower = zeroPoint! % 16;
+            var zeroPointUpper = zeroPoint! / 16;
             zeroPoint = torch.cat([zeroPointUpper, zeroPointLower], 0).to(ScalarType.Float32);
             zeroPoint -= 8;
-            var scale = this.get_buffer("scale").to(ScalarType.Float32);
+            var scale = this.get_buffer("scale")!.to(ScalarType.Float32);
             var restoreWeight = (weight - zeroPoint.view(-1, 1)) / scale.view(-1, 1);
             // use float32
             var result = torch.matmul(input.to(ScalarType.Float32), restoreWeight.T);
diff --git a/src/Microsoft.ML.GenAI.Core/Module/RotaryEmbedding.cs b/src/Microsoft.ML.GenAI.Core/Module/RotaryEmbedding.cs
index 8e06c838d5..fa86164a6a 100644
--- a/src/Microsoft.ML.GenAI.Core/Module/RotaryEmbedding.cs
+++ b/src/Microsoft.ML.GenAI.Core/Module/RotaryEmbedding.cs
@@ -109,7 +109,7 @@ public override RotaryEmbeddingOutput forward(RotaryEmbeddingInput input)
         var seqLen = input.SeqLen;
         // TODO
         // can be calculated once and cached
-        var invFreq = this.get_buffer("inv_freq").to(x.device);
+        var invFreq = this.get_buffer("inv_freq")!.to(x.device);
         var invFreqExpanded = invFreq.unsqueeze(0).unsqueeze(-1);
         invFreqExpanded = invFreqExpanded.expand(new long[] { positionIds.shape[0], -1, 1 });
         var positionIdsExpanded = positionIds.unsqueeze(1).to(torch.float32);
diff --git a/src/Microsoft.ML.TorchSharp/AutoFormerV2/ConvModule.cs b/src/Microsoft.ML.TorchSharp/AutoFormerV2/ConvModule.cs
index 8568239fc5..cfb9cf48da 100644
--- a/src/Microsoft.ML.TorchSharp/AutoFormerV2/ConvModule.cs
+++ b/src/Microsoft.ML.TorchSharp/AutoFormerV2/ConvModule.cs
@@ -35,7 +35,7 @@ public class ConvModule : Module<Tensor, Tensor>
         public ConvModule(int inChannel, int outChannel, int kernelSize, int stride = 1, int padding = 0, int dilation = 1, bool bias = true, bool useRelu = true)
             : base(nameof(ConvModule))
         {
-            this.conv = nn.Conv2d(in_channels: inChannel, out_channels: outChannel, kernelSize: kernelSize, stride: stride, padding: padding, dilation: dilation, bias: bias);
+            this.conv = nn.Conv2d(in_channels: inChannel, out_channels: outChannel, kernel_size: kernelSize, stride: stride, padding: padding, dilation: dilation, bias: bias);
             this.useRelu = useRelu;
             if (this.useRelu)
             {