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Arm backend: Correct links in vgf docs #21381

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wwwind merged 1 commit into from
Jul 24, 2026
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Arm backend: Correct links in vgf docs #21381

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4 changes: 2 additions & 2 deletions backends/arm/scripts/docgen/vgf/backends-arm-vgf-overview.md.in
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Expand Up @@ -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.

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4 changes: 2 additions & 2 deletions docs/source/backends/arm-vgf/arm-vgf-overview.md
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Expand Up @@ -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.

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