diff --git a/android/README.md b/android/README.md
index 6b8000c13fcc..f0c74750522d 100644
--- a/android/README.md
+++ b/android/README.md
@@ -2,7 +2,7 @@
 
 ## Demo applications and tutorials
 
-Please refer to [pytorch-labs/executorch-examples](https://github.com/pytorch-labs/executorch-examples/tree/main/dl3/android/DeepLabV3Demo) for the Android demo app based on [ExecuTorch](https://github.com/pytorch/executorch).
+Please refer to [meta-pytorch/executorch-examples](https://github.com/meta-pytorch/executorch-examples/tree/main/dl3/android/DeepLabV3Demo) for the Android demo app based on [ExecuTorch](https://github.com/pytorch/executorch).
 
 Please join our [Discord](https://discord.com/channels/1334270993966825602/1349854760299270284) for any questions.
 
diff --git a/aten/src/ATen/native/cuda/int4mm.cu b/aten/src/ATen/native/cuda/int4mm.cu
index 272eb9b9c564..5444bb57eba7 100644
--- a/aten/src/ATen/native/cuda/int4mm.cu
+++ b/aten/src/ATen/native/cuda/int4mm.cu
@@ -1304,7 +1304,7 @@ at::Tensor _convert_weight_to_int4pack_cuda(
   constexpr int32_t kKTileSize = 16;
 
   // GPT-FAST assumes nTileSize of 8 for quantized weight tensor.
-  // See https://github.com/pytorch-labs/gpt-fast/blob/091515ab5b06f91c0d6a3b92f9c27463f738cc9b/quantize.py#L510
+  // See https://github.com/meta-pytorch/gpt-fast/blob/091515ab5b06f91c0d6a3b92f9c27463f738cc9b/quantize.py#L510
   // Torch dynamo also requires the torch ops has the same output shape for each device.
   // See https://github.com/pytorch/pytorch/blob/ec284d3a74ec1863685febd53687d491fd99a161/torch/_meta_registrations.py#L3263
   constexpr int32_t kNTileSizeTensor = 8;
diff --git a/torch/testing/_internal/common_quantization.py b/torch/testing/_internal/common_quantization.py
index 211b282c4fc4..f8671379950e 100644
--- a/torch/testing/_internal/common_quantization.py
+++ b/torch/testing/_internal/common_quantization.py
@@ -611,7 +611,7 @@ def _group_quantize_tensor_symmetric(w, n_bit=4, groupsize=32):
 
 
 def _dynamically_quantize_per_channel(x, quant_min, quant_max, target_dtype):
-    # source: https://github.com/pytorch-labs/gpt-fast/blob/main/quantize.py
+    # source: https://github.com/meta-pytorch/gpt-fast/blob/main/quantize.py
     # default setup for affine quantization of activations
     x_dtype = x.dtype
     x = x.float()