From a3975cea3649ef07d1b1468fd89d10e92316654f Mon Sep 17 00:00:00 2001 From: Elena Zhelezina Date: Wed, 22 Jul 2026 15:22:44 +0100 Subject: [PATCH] Arm backend: Correct links in vgf docs Signed-off-by: Elena Zhelezina Change-Id: I15e5755d25875e2b4bcbe4b3245de2c15a030ca7 --- .../arm/scripts/docgen/vgf/backends-arm-vgf-overview.md.in | 4 ++-- docs/source/backends/arm-vgf/arm-vgf-overview.md | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/backends/arm/scripts/docgen/vgf/backends-arm-vgf-overview.md.in b/backends/arm/scripts/docgen/vgf/backends-arm-vgf-overview.md.in index 53af3e2499d..3d3a5ad2707 100644 --- a/backends/arm/scripts/docgen/vgf/backends-arm-vgf-overview.md.in +++ b/backends/arm/scripts/docgen/vgf/backends-arm-vgf-overview.md.in @@ -44,8 +44,8 @@ See [Partitioner API](arm-vgf-partitioner.md) for more information of the Partit ## Quantization -The VGF quantizer supports [Post Training Quantization (PT2E)](https://docs.pytorch.org/ao/main/tutorials_source/pt2e_quant_ptq.html) -and [Quantization-Aware Training (QAT)](https://docs.pytorch.org/ao/main/tutorials_source/pt2e_quant_qat.html). +The VGF quantizer supports [Post Training Quantization (PT2E)](https://docs.pytorch.org/ao/main/pt2e_quantization/pt2e_quant_ptq.html) +and [Quantization-Aware Training (QAT)](https://docs.pytorch.org/ao/main/pt2e_quantization/pt2e_quant_qat.html). Partial quantization is supported, allowing users to quantize only specific parts of the model while leaving others in floating-point. diff --git a/docs/source/backends/arm-vgf/arm-vgf-overview.md b/docs/source/backends/arm-vgf/arm-vgf-overview.md index 110c50af55c..2d5d8dc571b 100644 --- a/docs/source/backends/arm-vgf/arm-vgf-overview.md +++ b/docs/source/backends/arm-vgf/arm-vgf-overview.md @@ -112,8 +112,8 @@ See [Partitioner API](arm-vgf-partitioner.md) for more information of the Partit ## Quantization -The VGF quantizer supports [Post Training Quantization (PT2E)](https://docs.pytorch.org/ao/main/tutorials_source/pt2e_quant_ptq.html) -and [Quantization-Aware Training (QAT)](https://docs.pytorch.org/ao/main/tutorials_source/pt2e_quant_qat.html). +The VGF quantizer supports [Post Training Quantization (PT2E)](https://docs.pytorch.org/ao/main/pt2e_quantization/pt2e_quant_ptq.html) +and [Quantization-Aware Training (QAT)](https://docs.pytorch.org/ao/main/pt2e_quantization/pt2e_quant_qat.html). Partial quantization is supported, allowing users to quantize only specific parts of the model while leaving others in floating-point.