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README.md

@@ -18,8 +18,6 @@ pipeline_tag: text-generation
 
 # <span style="color: #7FFF7F;">RWKV7-Goose-World3-2.9B-HF GGUF Models</span>
 
-Note:  you must use latest llama.cpp https://github.com/ggml-org/llama.cpp to run this model with llama.cpp
-
 ## **Choosing the Right Model Format**  
 
 Selecting the correct model format depends on your **hardware capabilities** and **memory constraints**.  
@@ -27,7 +25,7 @@ Selecting the correct model format depends on your **hardware capabilities** and
 ### **BF16 (Brain Float 16) – Use if BF16 acceleration is available**  
 - A 16-bit floating-point format designed for **faster computation** while retaining good precision.  
 - Provides **similar dynamic range** as FP32 but with **lower memory usage**.  
-- Recommended if your hardware supports **BF16 acceleration** (check your device’s specs).  
+- Recommended if your hardware supports **BF16 acceleration** (check your device's specs).  
 - Ideal for **high-performance inference** with **reduced memory footprint** compared to FP32.  
 
 📌 **Use BF16 if:**  
@@ -99,7 +97,7 @@ These models are optimized for **extreme memory efficiency**, making them ideal
 | Model Format  | Precision  | Memory Usage  | Device Requirements  | Best Use Case  |  
 |--------------|------------|---------------|----------------------|---------------|  
 | **BF16**     | Highest    | High          | BF16-supported GPU/CPUs  | High-speed inference with reduced memory |  
-| **F16**      | High       | High          | FP16-supported devices | GPU inference when BF16 isn’t available |  
+| **F16**      | High       | High          | FP16-supported devices | GPU inference when BF16 isn't available |  
 | **Q4_K**     | Medium Low | Low           | CPU or Low-VRAM devices | Best for memory-constrained environments |  
 | **Q6_K**     | Medium     | Moderate      | CPU with more memory | Better accuracy while still being quantized |  
 | **Q8_0**     | High       | Moderate      | CPU or GPU with enough VRAM | Best accuracy among quantized models |  
@@ -159,23 +157,45 @@ These models are optimized for **extreme memory efficiency**, making them ideal
 - Prefer IQ4_NL for better accuracy.
 
 # <span id="testllm" style="color: #7F7FFF;">🚀 If you find these models useful</span>
-
-Please click like ❤ . Also I’d really appreciate it if you could test my Network Monitor Assistant at 👉 [Network Monitor Assitant](https://freenetworkmonitor.click/dashboard).
-
-💬 Click the **chat icon** (bottom right of the main and dashboard pages) . Choose a LLM; toggle between the LLM Types TurboLLM -> FreeLLM -> TestLLM.
-
-### What I'm Testing
-
-I'm experimenting with **function calling** against my network monitoring service. Using small open source models. I am into the question "How small can it go and still function".
-
-🟡 **TestLLM** – Runs the current testing model using llama.cpp on 6 threads of a Cpu VM (Should take about 15s to load. Inference speed is quite slow and it only processes one user prompt at a time—still working on scaling!). If you're curious, I'd be happy to share how it works! .
-
-### The other Available AI Assistants
-
-🟢 **TurboLLM** – Uses **gpt-4o-mini** Fast! . Note: tokens are limited since OpenAI models are pricey, but you can [Login](https://freenetworkmonitor.click) or [Download](https://freenetworkmonitor.click/download) the Free Network Monitor agent to get more tokens, Alternatively use the TestLLM .
-
-🔵 **HugLLM** – Runs **open-source Hugging Face models** Fast, Runs small models (≈8B) hence lower quality, Get 2x more tokens (subject to Hugging Face API availability)
-
+❤ **Please click "Like" if you find this useful!**  
+Help me test my **AI-Powered Network Monitor Assistant** with **quantum-ready security checks**:  
+👉 [Free Network Monitor](https://freenetworkmonitor.click/dashboard)  
+
+💬 **How to test**:  
+1. Click the **chat icon** (bottom right on any page)  
+2. Choose an **AI assistant type**:  
+   - `TurboLLM` (GPT-4-mini)  
+   - `FreeLLM` (Open-source)  
+   - `TestLLM` (Experimental CPU-only)  
+
+### **What I’m Testing**  
+I’m pushing the limits of **small open-source models for AI network monitoring**, specifically:  
+- **Function calling** against live network services  
+- **How small can a model go** while still handling:  
+  - Automated **Nmap scans**  
+  - **Quantum-readiness checks**  
+  - **Metasploit integration**  
+
+🟡 **TestLLM** – Current experimental model (llama.cpp on 6 CPU threads):  
+- ✅ **Zero-configuration setup**  
+- ⏳ 30s load time (slow inference but **no API costs**)  
+- 🔧 **Help wanted!** If you’re into **edge-device AI**, let’s collaborate!  
+
+### **Other Assistants**  
+🟢 **TurboLLM** – Uses **gpt-4-mini** for:  
+- **Real-time network diagnostics**  
+- **Automated penetration testing** (Nmap/Metasploit)  
+- 🔑 Get more tokens by [downloading our Free Network Monitor Agent](https://freenetworkmonitor.click/download)  
+
+🔵 **HugLLM** – Open-source models (≈8B params):  
+- **2x more tokens** than TurboLLM  
+- **AI-powered log analysis**  
+- 🌐 Runs on Hugging Face Inference API  
+
+### 💡 **Example AI Commands to Test**:  
+1. `"Give me info on my websites SSL certificate"`  
+2. `"Check if my server is using quantum safe encyption for communication"`  
+3. `"Run a quick Nmap vulnerability test"`