diff --git a/README.md b/README.md
index 66f861d..6b5a316 100644
--- a/README.md
+++ b/README.md
@@ -37,14 +37,20 @@ tags:
 - thinking
 - reasoning
 - qwen3_moe
+- not-for-all-audiences
+- uncensored
+- abliterated
 base_model:
 - kalomaze/Qwen3-16B-A3B
-- huihui-ai/Moonlight-16B-A3B-Instruct-abliterated
+- huihui-ai/Qwen3-16B-A3B-abliterated
 pipeline_tag: text-generation
 ---
 
 (quants uploading, examples to be added...)
 
+<B><font color="red">WARNING:</font> NSFW. Vivid prose. INTENSE. Visceral Details. Violence. HORROR. GORE. Swearing. UNCENSORED... humor, romance, fun. </B>
+
+
 <h2>Qwen3-18B-A3B-Stranger-Thoughts-Abliterated-Uncensored-GGUF</h2>
 
 <img src="qwen-18b.jpg" style="float:right; width:300px; height:300px; padding:10px;">
@@ -123,6 +129,26 @@ I found the best results during testing (stable thinking, generation, minimal is
 - temp range .6 to 1.2 ; with outside max of 1.8
 - context of 8k min 
   
+<B>IMPORTANT: Using an "abliterated" model VS "uncensored" model</B>
+
+Usually when you a tell a model to generate horror, swear or x-rated content this is all you have to do to get said content type.
+
+In the case of this model, it will not refuse your request, however it needs to be "pushed" a bit / directed a bit more in SOME CASES.
+
+See examples #1 and #2 below show this.
+
+Although this model will generated x-rated content too, likewise you need to tell it to use "slang" (and include the terms you want)
+to get it generate the content correctly as the "expected" content level too.
+
+Without these added directive(s), the content can be "bland" by comparison to an "uncensored model" or model trained on uncensored content.
+
+Roughly, the model tries to generate the content but the "default" setting(s) are so "tame" it needs a push to generate at expected graphic,
+cursing or explicit levels.
+
+Even with minimal direction (ie, use these words to swear: x,y,z), this will be enough to push the model to generate the requested content in the ahh... expected format.
+
+---
+
 You may find that Qwen's default parameters/samplers also work better for your use case(s) too.
 
 Please refer to the orginal model repo for all default settings, templates, benchmarks, etc.: