From 345b6672d967e55a68ac5db4686362571a2f3f8c Mon Sep 17 00:00:00 2001
From: fria <138676274+friadev@users.noreply.github.com>
Date: Tue, 19 Nov 2024 09:48:38 -0600
Subject: [PATCH] remove bold

Signed-off-by: fria <138676274+friadev@users.noreply.github.com>
---
 docs/ai-chat.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/ai-chat.md b/docs/ai-chat.md
index a4efe807a9..acb1751a1b 100755
--- a/docs/ai-chat.md
+++ b/docs/ai-chat.md
@@ -41,7 +41,7 @@ To run AI locally, you need both an AI model and an AI client.
 
 ### Choosing a Model
 
-There are many permissively licensed models available to download. **[Hugging Face](https://huggingface.co/models)** is a platform that lets you browse, research, and download models in common formats like [GGUF](https://huggingface.co/docs/hub/en/gguf). Companies that provide good open-weights models include big names like Mistral, Meta, Microsoft, and Google. However, there are also many community models and 'fine-tunes' available. As mentioned above, quantized models offer the best balance between model quality and performance for those using consumer-grade hardware.
+There are many permissively licensed models available to download. [Hugging Face](https://huggingface.co/models) is a platform that lets you browse, research, and download models in common formats like [GGUF](https://huggingface.co/docs/hub/en/gguf). Companies that provide good open-weights models include big names like Mistral, Meta, Microsoft, and Google. However, there are also many community models and 'fine-tunes' available. As mentioned above, quantized models offer the best balance between model quality and performance for those using consumer-grade hardware.
 
 To help you choose a model that fits your needs, you can look at leaderboards and benchmarks, of which there are many kinds. The most widely-used leaderboard is the community-driven [LM Arena](https://lmarena.ai). Additionally, the [OpenLLM Leaderboard](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) focuses on the performance of open-weights models on common benchmarks like [MMLU-Pro](https://arxiv.org/abs/2406.01574).  Furthermore, there are also specialized benchmarks which measure factors like [emotional intelligence](https://eqbench.com), ["uncensored general intelligence"](https://huggingface.co/spaces/DontPlanToEnd/UGI-Leaderboard), and [many others](https://www.nebuly.com/blog/llm-leaderboards).