From 12d9a677527a3fa1d486299805d2ee8296b52a33 Mon Sep 17 00:00:00 2001 From: Merve Noyan Date: Thu, 7 Sep 2023 16:46:05 +0200 Subject: [PATCH] Fix inline latex --- docs/source/conceptual/quantization.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/conceptual/quantization.md b/docs/source/conceptual/quantization.md index e14e7c18..f184f9ec 100644 --- a/docs/source/conceptual/quantization.md +++ b/docs/source/conceptual/quantization.md @@ -6,7 +6,7 @@ TGI offers GPTQ and bits-and-bytes quantization to quantize large language model GPTQ is a post-training quantization method to make the model smaller. It quantizes each weight by finding a compressed version of that weight, that will yield a minimum mean squared error like below 👇 -Given a layer \(l\) with weight matrix \(W_{l}\) and layer input \(X_{l}\), find quantized weight \(\hat{W}_{l}\): +Given a layer \\(l\\) with weight matrix \\(W_{l}\\) and layer input \\(X_{l}\\), find quantized weight \\(\\hat{W}_{l}\\): $$({\hat{W}_{l}}^{*} = argmin_{\hat{W_{l}}} ||W_{l}X-\hat{W}_{l}X||^{2}_{2})$$