Remove CORE prefix: Apex is reasoning, CORE lineup is tooling

README.md

@@ -16,14 +16,14 @@ tags:
 - thinking
 - chain-of-thought
 base_model: TrevorJS/gemma-4-26B-A4B-it-uncensored
-model_name: BugTraceAI-CORE-G4-Apex
+model_name: BugTraceAI-G4-Apex
 ---
 
-# 🌋 BugTraceAI-CORE-G4-Apex (26B MoE)
+# 🌋 BugTraceAI-G4-Apex (26B MoE)
 
 **The Apex Predator of Offensive Security Reasoning.**
 
-BugTraceAI-CORE-G4-Apex is a high-performance, uncensored 26B Mixture-of-Experts (MoE) model based on Gemma 4 architecture. It has been meticulously fine-tuned via **DPO (Direct Preference Optimization)** on a curated "Super Dataset" comprising elite Bug Bounty reports, advanced malware methodologies, and multi-layer WAF evasion techniques.
+BugTraceAI-G4-Apex is a high-performance, uncensored 26B Mixture-of-Experts (MoE) model based on Gemma 4 architecture. It has been meticulously fine-tuned via **DPO (Direct Preference Optimization)** on a curated "Super Dataset" comprising elite Bug Bounty reports, advanced malware methodologies, and multi-layer WAF evasion techniques.
 
 Unlike standard security models, the Apex variant features an injected **Opus-style reasoning engine**, forcing the model to perform a deep step-by-step analysis inside a `<thinking>` block before providing technical payloads or remediation strategies.
 
@@ -48,7 +48,7 @@ To maximize reasoning performance and reduce VRAM overhead, we have **manually s
 | :--- | :--- |
 | **Organization** | BugTraceAI |
 | **Framework** | BugTraceAI Apex Security Framework |
-| **Variant** | BugTraceAI-CORE-G4-Apex |
+| **Variant** | BugTraceAI-G4-Apex |
 | **Parameter Scale** | 26B (Mixture of Experts) |
 | **Architecture** | Gemma 4 (Text-Only Optimized) |
 | **Fine-tuning** | DPO (Direct Preference Optimization) |
@@ -110,6 +110,18 @@ PARAMETER temperature 0.1
 PARAMETER top_p 0.9
 ```
 
+### 🖥️ LM Studio Deployment Guide (IMPORTANT)
+
+> ⚠️ **WARNING - AVOID COUNTERFEIT QUANTS:** We are aware of third-party repositories uploading broken GGUF versions of this model with inflated metadata (256k context). Those versions **WILL FAIL TO LOAD** on 12GB/16GB VRAM cards due to extreme memory allocation. Always use the official BugTraceAI files hosted here to ensure the architecture is correctly formatted.
+
+When loading the GGUF file in LM Studio (Version 0.4.11 or higher required), you **must manually limit the context** to prevent VRAM allocation crashes caused by the architecture's native 256k context capacity.
+
+1.  Load the model and locate the settings panel on the right.
+2.  Set **Context Length** to `8192` (or up to `16384` if you have 24GB+ VRAM).
+3.  Set **GPU Offload** to `10-14` (for 12GB VRAM cards like RTX 3060).
+4.  Ensure **Unified KV Cache** is turned **OFF**.
+5.  Click Load.
+
 ### 🐍 Usage in Google Colab (llama-cpp-python)
 
 If you are using the "Open in Colab" button on Hugging Face, replace the generation cell with the following code to ensure the **Apex Reasoning Engine** and precise parameters are activated: