diff --git a/docs/deep-learning/torch_support.md b/docs/deep-learning/torch_support.md
index 383497a99..d91ef60c5 100644
--- a/docs/deep-learning/torch_support.md
+++ b/docs/deep-learning/torch_support.md
@@ -5,7 +5,7 @@ In addition to the built-in models, Concrete ML supports generic machine learnin
 There are two approaches to build [FHE-compatible deep networks](../getting-started/concepts.md#model-accuracy-considerations-under-fhe-constraints):
 
 - [Quantization Aware Training (QAT)](../explanations/quantization.md) requires using custom layers, but can quantize weights and activations to low bit-widths. Concrete ML works with [Brevitas](../explanations/inner-workings/external_libraries.md#brevitas), a library providing QAT support for PyTorch. To use this mode, compile models using `compile_brevitas_qat_model`
-- Post-training Quantization: in this mode a vanilla PyTorch model can be compiled. However, when quantizing weights & activations to fewer than 7 bits the accuracy can decrease strongly. On the other hand, depending on the model size, quantizing with 6-8 bits can be incompatible with FHE constraints. To use this mode, compile models with `compile_torch_model`.
+- **Post-training Quantization**: This mode allows a vanilla PyTorch model to be compiled. However, when quantizing weights & activations to fewer than 7 bits, the accuracy can decrease strongly. On the other hand, depending on the model size, quantizing with 6-8 bits can be incompatible with FHE constraints. To use this mode, compile models with `compile_torch_model`.
 
 Both approaches should be used with the `rounding_threshold_bits` parameter set accordingly. The best values for this parameter need to be determined through experimentation. A good initial value to try is `6`. See [here](../explanations/advanced_features.md#rounded-activations-and-quantizers) for more details.