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README.md

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+---
+thumbnail: >-
+  https://huggingface.co/PocketDoc/Dans-PersonalityEngine-V1.3.0-24b/resolve/main/resources/pe.png
+license: apache-2.0
+tags:
+- general-purpose
+- roleplay
+- storywriting
+- chemistry
+- biology
+- code
+- climate
+- axolotl
+- text-generation-inference
+- finetune
+- legal
+- medical
+- finance
+datasets:
+- PocketDoc/Dans-Prosemaxx-RP
+- PocketDoc/Dans-Personamaxx-Logs-2
+- PocketDoc/Dans-Personamaxx-VN
+- PocketDoc/Dans-Kinomaxx-VanillaBackrooms
+- PocketDoc/Dans-Prosemaxx-Gutenberg
+- PocketDoc/Dans-Prosemaxx-Cowriter-3-XL
+- PocketDoc/Dans-Prosemaxx-Adventure
+- PocketDoc/Dans-Failuremaxx-Adventure-3
+- PocketDoc/Dans-Prosemaxx-InstructWriter-ZeroShot-2
+- PocketDoc/Dans-Prosemaxx-InstructWriter-ZeroShot-3
+- PocketDoc/Dans-Prosemaxx-InstructWriter-Continue-2
+- PocketDoc/Dans-Prosemaxx-Instructwriter-Long
+- PocketDoc/Dans-Prosemaxx-RepRemover-1
+- PocketDoc/Dans-MemoryCore-CoreCurriculum-Small
+- AquaV/US-Army-Survival-Sharegpt
+- AquaV/Multi-Environment-Operations-Sharegpt
+- AquaV/Resistance-Sharegpt
+- AquaV/Interrogation-Sharegpt
+- AquaV/Chemical-Biological-Safety-Applications-Sharegpt
+- AquaV/Energetic-Materials-Sharegpt
+- PocketDoc/Dans-Mathmaxx
+- PJMixers/Math-Multiturn-1K-ShareGPT
+- PocketDoc/Dans-Taskmaxx
+- PocketDoc/Dans-Taskmaxx-DataPrepper
+- PocketDoc/Dans-Taskmaxx-ConcurrentQA-Reworked
+- PocketDoc/Dans-Taskmaxx-TableGPT
+- PocketDoc/Dans-Taskmaxx-SciRIFF
+- PocketDoc/Dans-Taskmaxx-Edit
+- PocketDoc/Dans-Toolmaxx-Agent
+- PocketDoc/Dans-Toolmaxx-ShellCommands
+- PocketDoc/Dans-Toolmaxx-Functions-Toolbench
+- PocketDoc/Dans-Toolmaxx-Functions-ToolACE
+- PocketDoc/Dans-Toolmaxx-Functions-apigen-subset
+- PocketDoc/Dans-Assistantmaxx-OpenAssistant2
+- PocketDoc/Dans-Assistantmaxx-Opus-Merge-2
+- PocketDoc/Dans-Assistantmaxx-sonnetorca-subset
+- PocketDoc/Dans-Assistantmaxx-sonnetorca-subset-2
+- PocketDoc/Dans-Assistantmaxx-Synthia
+- PocketDoc/Dans-Assistantmaxx-ASL
+- PocketDoc/Dans-Assistantmaxx-PersonaLLM-Opus
+- PocketDoc/Dans-Assistantmaxx-LongAlign
+- PocketDoc/Dans-Assistantmaxx-OpenLeecher-Instruct
+- PocketDoc/Dans-Assistantmaxx-Tulu3-IF
+- PocketDoc/Dans-Systemmaxx
+- PocketDoc/Dans-Logicmaxx-SAT-AP
+- PJMixers/grimulkan_theory-of-mind-ShareGPT
+- PJMixers/grimulkan_physical-reasoning-ShareGPT
+- PocketDoc/Dans-Reasoningmaxx-NaturalReasoning
+- PocketDoc/Dans-Reasoningmaxx-WebInstruct
+- PocketDoc/Dans-Reasoningmaxx-GeneralReasoning
+- PocketDoc/Dans-Assistantmaxx-ClosedInstruct
+language:
+- en
+- ar
+- de
+- fr
+- es
+- hi
+- pt
+- ja
+- ko
+base_model:
+- mistralai/Mistral-Small-3.1-24B-Base-2503
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+# <span style="color: #7FFF7F;">Dans-PersonalityEngine-V1.3.0-24b GGUF Models</span>
+
+
+## <span style="color: #7F7FFF;">Model Generation Details</span>
+
+This model was generated using [llama.cpp](https://github.com/ggerganov/llama.cpp) at commit [`f5cd27b7`](https://github.com/ggerganov/llama.cpp/commit/f5cd27b71da3ac375a04a41643d14fc779a8057b).
+
+
+
+
+## <span style="color: #7FFF7F;">Ultra-Low-Bit Quantization with IQ-DynamicGate (1-2 bit)</span>
+
+Our latest quantization method introduces **precision-adaptive quantization** for ultra-low-bit models (1-2 bit), with benchmark-proven improvements on **Llama-3-8B**. This approach uses layer-specific strategies to preserve accuracy while maintaining extreme memory efficiency.
+
+### **Benchmark Context**
+All tests conducted on **Llama-3-8B-Instruct** using:
+- Standard perplexity evaluation pipeline
+- 2048-token context window
+- Same prompt set across all quantizations
+
+### **Method**
+- **Dynamic Precision Allocation**:  
+  - First/Last 25% of layers → IQ4_XS (selected layers)  
+  - Middle 50% → IQ2_XXS/IQ3_S (increase efficiency)  
+- **Critical Component Protection**:  
+  - Embeddings/output layers use Q5_K  
+  - Reduces error propagation by 38% vs standard 1-2bit  
+
+### **Quantization Performance Comparison (Llama-3-8B)**
+
+| Quantization | Standard PPL | DynamicGate PPL | Δ PPL   | Std Size | DG Size | Δ Size | Std Speed | DG Speed |
+|--------------|--------------|------------------|---------|----------|---------|--------|-----------|----------|
+| IQ2_XXS      | 11.30        | 9.84             | -12.9%  | 2.5G     | 2.6G    | +0.1G  | 234s      | 246s     |
+| IQ2_XS       | 11.72        | 11.63            | -0.8%   | 2.7G     | 2.8G    | +0.1G  | 242s      | 246s     |
+| IQ2_S        | 14.31        | 9.02             | -36.9%  | 2.7G     | 2.9G    | +0.2G  | 238s      | 244s     |
+| IQ1_M        | 27.46        | 15.41            | -43.9%  | 2.2G     | 2.5G    | +0.3G  | 206s      | 212s     |
+| IQ1_S        | 53.07        | 32.00            | -39.7%  | 2.1G     | 2.4G    | +0.3G  | 184s      | 209s     |
+
+**Key**:
+- PPL = Perplexity (lower is better)
+- Δ PPL = Percentage change from standard to DynamicGate
+- Speed = Inference time (CPU avx2, 2048 token context)
+- Size differences reflect mixed quantization overhead
+
+**Key Improvements:**
+- 🔥 **IQ1_M** shows massive 43.9% perplexity reduction (27.46 → 15.41)
+- 🚀 **IQ2_S** cuts perplexity by 36.9% while adding only 0.2GB
+- ⚡ **IQ1_S** maintains 39.7% better accuracy despite 1-bit quantization
+
+**Tradeoffs:**
+- All variants have modest size increases (0.1-0.3GB)
+- Inference speeds remain comparable (<5% difference)
+
+
+### **When to Use These Models**
+📌 **Fitting models into GPU VRAM**
+
+✔ **Memory-constrained deployments**
+
+✔ **Cpu and Edge Devices** where 1-2bit errors can be tolerated 
+ 
+✔ **Research** into ultra-low-bit quantization
+
+
+
+## **Choosing the Right Model Format**  
+
+Selecting the correct model format depends on your **hardware capabilities** and **memory constraints**.  
+
+### **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).  
+- Ideal for **high-performance inference** with **reduced memory footprint** compared to FP32.  
+
+📌 **Use BF16 if:**  
+✔ Your hardware has native **BF16 support** (e.g., newer GPUs, TPUs).  
+✔ You want **higher precision** while saving memory.  
+✔ You plan to **requantize** the model into another format.  
+
+📌 **Avoid BF16 if:**  
+❌ Your hardware does **not** support BF16 (it may fall back to FP32 and run slower).  
+❌ You need compatibility with older devices that lack BF16 optimization.  
+
+---
+
+### **F16 (Float 16) – More widely supported than BF16**  
+- A 16-bit floating-point **high precision** but with less of range of values than BF16. 
+- Works on most devices with **FP16 acceleration support** (including many GPUs and some CPUs).  
+- Slightly lower numerical precision than BF16 but generally sufficient for inference.  
+
+📌 **Use F16 if:**  
+✔ Your hardware supports **FP16** but **not BF16**.  
+✔ You need a **balance between speed, memory usage, and accuracy**.  
+✔ You are running on a **GPU** or another device optimized for FP16 computations.  
+
+📌 **Avoid F16 if:**  
+❌ Your device lacks **native FP16 support** (it may run slower than expected).  
+❌ You have memory limitations.  
+
+---
+
+### **Quantized Models (Q4_K, Q6_K, Q8, etc.) – For CPU & Low-VRAM Inference**  
+Quantization reduces model size and memory usage while maintaining as much accuracy as possible.  
+- **Lower-bit models (Q4_K)** → **Best for minimal memory usage**, may have lower precision.  
+- **Higher-bit models (Q6_K, Q8_0)** → **Better accuracy**, requires more memory.  
+
+📌 **Use Quantized Models if:**  
+✔ You are running inference on a **CPU** and need an optimized model.  
+✔ Your device has **low VRAM** and cannot load full-precision models.  
+✔ You want to reduce **memory footprint** while keeping reasonable accuracy.  
+
+📌 **Avoid Quantized Models if:**  
+❌ You need **maximum accuracy** (full-precision models are better for this).  
+❌ Your hardware has enough VRAM for higher-precision formats (BF16/F16).  
+
+---
+
+### **Very Low-Bit Quantization (IQ3_XS, IQ3_S, IQ3_M, Q4_K, Q4_0)**  
+These models are optimized for **extreme memory efficiency**, making them ideal for **low-power devices** or **large-scale deployments** where memory is a critical constraint.  
+
+- **IQ3_XS**: Ultra-low-bit quantization (3-bit) with **extreme memory efficiency**.  
+  - **Use case**: Best for **ultra-low-memory devices** where even Q4_K is too large.  
+  - **Trade-off**: Lower accuracy compared to higher-bit quantizations.  
+
+- **IQ3_S**: Small block size for **maximum memory efficiency**.  
+  - **Use case**: Best for **low-memory devices** where **IQ3_XS** is too aggressive.  
+
+- **IQ3_M**: Medium block size for better accuracy than **IQ3_S**.  
+  - **Use case**: Suitable for **low-memory devices** where **IQ3_S** is too limiting.  
+
+- **Q4_K**: 4-bit quantization with **block-wise optimization** for better accuracy.  
+  - **Use case**: Best for **low-memory devices** where **Q6_K** is too large.  
+
+- **Q4_0**: Pure 4-bit quantization, optimized for **ARM devices**.  
+  - **Use case**: Best for **ARM-based devices** or **low-memory environments**.  
+
+---
+
+### **Summary Table: Model Format Selection**  
+
+| 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 |  
+| **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 |  
+| **IQ3_XS**   | Very Low   | Very Low      | Ultra-low-memory devices | Extreme memory efficiency and low accuracy |  
+| **Q4_0**     | Low        | Low           | ARM or low-memory devices | llama.cpp can optimize for ARM devices |  
+
+---
+
+## **Included Files & Details**  
+
+### `Dans-PersonalityEngine-V1.3.0-24b-bf16.gguf`  
+- Model weights preserved in **BF16**.  
+- Use this if you want to **requantize** the model into a different format.  
+- Best if your device supports **BF16 acceleration**.  
+
+### `Dans-PersonalityEngine-V1.3.0-24b-f16.gguf`  
+- Model weights stored in **F16**.  
+- Use if your device supports **FP16**, especially if BF16 is not available.  
+
+### `Dans-PersonalityEngine-V1.3.0-24b-bf16-q8_0.gguf`  
+- **Output & embeddings** remain in **BF16**.  
+- All other layers quantized to **Q8_0**.  
+- Use if your device supports **BF16** and you want a quantized version.  
+
+### `Dans-PersonalityEngine-V1.3.0-24b-f16-q8_0.gguf`  
+- **Output & embeddings** remain in **F16**.  
+- All other layers quantized to **Q8_0**.    
+
+### `Dans-PersonalityEngine-V1.3.0-24b-q4_k.gguf`  
+- **Output & embeddings** quantized to **Q8_0**.  
+- All other layers quantized to **Q4_K**.  
+- Good for **CPU inference** with limited memory.  
+
+### `Dans-PersonalityEngine-V1.3.0-24b-q4_k_s.gguf`  
+- Smallest **Q4_K** variant, using less memory at the cost of accuracy.  
+- Best for **very low-memory setups**.  
+
+### `Dans-PersonalityEngine-V1.3.0-24b-q6_k.gguf`  
+- **Output & embeddings** quantized to **Q8_0**.  
+- All other layers quantized to **Q6_K** .  
+
+### `Dans-PersonalityEngine-V1.3.0-24b-q8_0.gguf`  
+- Fully **Q8** quantized model for better accuracy.  
+- Requires **more memory** but offers higher precision.  
+
+### `Dans-PersonalityEngine-V1.3.0-24b-iq3_xs.gguf`  
+- **IQ3_XS** quantization, optimized for **extreme memory efficiency**.  
+- Best for **ultra-low-memory devices**.  
+
+### `Dans-PersonalityEngine-V1.3.0-24b-iq3_m.gguf`  
+- **IQ3_M** quantization, offering a **medium block size** for better accuracy.  
+- Suitable for **low-memory devices**.  
+
+### `Dans-PersonalityEngine-V1.3.0-24b-q4_0.gguf`  
+- Pure **Q4_0** quantization, optimized for **ARM devices**.  
+- Best for **low-memory environments**.
+- Prefer IQ4_NL for better accuracy.
+
+# <span id="testllm" style="color: #7F7FFF;">🚀 If you find these models useful</span>
+❤ **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://readyforquantum.com/dashboard/?assistant=open)  
+
+💬 **How to test**:  
+ Choose an **AI assistant type**:  
+   - `TurboLLM` (GPT-4o-mini)  
+   - `HugLLM` (Hugginface 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**  
+  - **Network Monitoring tasks**  
+
+🟡 **TestLLM** – Current experimental model (llama.cpp on 2 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-4o-mini** for: 
+- **Create custom cmd processors to run .net code on Free Network Monitor Agents**
+- **Real-time network diagnostics and monitoring**
+- **Security Audits**
+- **Penetration testing** (Nmap/Metasploit)  
+- 🔑 Get more tokens by logging in or [downloading our Free Network Monitor Agent with integrated AI Assistant](https://readyforquantum.com/download)  
+
+🔵 **HugLLM** – Latest Open-source models:  
+- 🌐 Runs on Hugging Face Inference API  
+
+### 💡 **Example commands to you could 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 comprehensive security audit on my server"`
+4. '"Create a cmd processor to .. (what ever you want)" Note you need to install a Free Network Monitor Agent to run the .net code from. This is a very flexible and powerful feature. Use with caution!
+
+
+<!doctype html>
+<html lang="en">
+    <head>
+        <meta charset="UTF-8" />
+        <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+        <title>Dans-PersonalityEngine-V1.3.0-24b</title>
+    </head>
+    <div class="crt-container">
+        <div class="crt-case">
+            <div class="crt-inner-case">
+                <div class="crt-bezel">
+                    <div class="terminal-screen">
+                        <div style="text-align: center">
+                            <h2>Dans-PersonalityEngine-V1.3.0-24b</h2>
+                            <pre class="code-block" style="display: inline-block; text-align: left; font-size: clamp(2px, 0.8vw, 14px); line-height: 1.2; max-width: 100%; overflow: hidden; white-space: pre;">
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+⠀⠀⠀⠀⠀⠀⠀⠀⡄⠆⠀⢠⠀⠛⣸⣄⣶⣾⡷⡾⠘⠃⢀⠀⣴⠀⡄⠰⢆⣠⠘⠰⠀⡀⠀⠀⠀⠀⠀
+⠀⠀⠀⠀⠀⠀⠀⠀⠀⠃⠀⡋⢀⣤⡿⠟⠋⠁⠀⡠⠤⢇⠋⠀⠈⠃⢀⠀⠈⡡⠤⠀⠀⠁⢄⠀⠀⠀⠀
+⠀⠀⠀⠀⠀⠁⡂⠀⠀⣀⣔⣧⠟⠋⠀⢀⡄⠀⠪⣀⡂⢁⠛⢆⠀⠀⠀⢎⢀⠄⢡⠢⠛⠠⡀⠀⠄⠀⠀
+⠀⠀⡀⠡⢑⠌⠈⣧⣮⢾⢏⠁⠀⠀⡀⠠⠦⠈⠀⠞⠑⠁⠀⠀⢧⡄⠈⡜⠷⠒⢸⡇⠐⠇⠿⠈⣖⠂⠀
+⠀⢌⠀⠤⠀⢠⣞⣾⡗⠁⠀⠈⠁⢨⡼⠀⠀⠀⢀⠀⣀⡤⣄⠄⠈⢻⡇⠀⠐⣠⠜⠑⠁⠀⣀⡔⡿⠨⡄
+⠈⠂⠀⠆⠀⣼⣾⠟⠀⠑⠀⡐⠗⠉⠀⠐⠶⣤⡵⠋⠀⠠⠹⡌⡀⠘⠇⢠⣾⡣⣀⡴⠋⠅⠈⢊⠠⡱⡀
+⠪⠑⢌⠂⣼⣿⡟⠀⠀⠙⠀⠀⠀⡀⠀⠀⠐⡞⡐⠀⠀⡧⠀⢀⠠⠀⣁⠾⡇⠀⠙⡁⠀⠀⢀⣨⣄⡠⢱
+⣸⠈⠊⠙⣛⣿⡧⠔⠚⠛⠳⣄⣀⡬⠤⠬⠼⡣⠃⠀⢀⡗⠀⡤⠞⠙⠄⠂⠃⢀⣠⣤⠶⠙⠅⠁⠃⠋⠈
+⢋⠼⣀⠰⢯⢿⠁⠀⢢⠀⠀⢐⠋⡀⠀⠈⠁⠀⣀⣰⠏⠒⠙⠈⠀⣀⡤⠞⢁⣼⠏⠘⢀⣀⢤⢤⡐⢈⠂
+⠀⠢⠀⠀⠸⣿⡄⠲⠚⠘⠚⠃⢀⠀⠈⢋⠶⠛⠉⠉⢃⣀⢤⢾⠋⣁⡤⡚⠁⢹⠁⠠⢛⠠⠬⠁⢬⠀⠀
+⠀⠈⢳⣒⠋⠉⣿⢐⠠⣀⣃⠀⠀⠉⠂⢁⣀⣀⡤⢞⠩⢑⡨⠰⡞⠁⠁⢀⡠⠾⠎⡈⡌⡈⡓⡀⠄⠀⠀
+⠀⠀⠀⠉⠘⠃⢻⡒⠦⢼⣿⣛⣻⣿⡷⢄⣀⣀⣠⣴⢾⣿⣆⣡⡄⣠⣪⡿⣷⣾⣷⣧⡡⠅⣇⠍⠀⠀⠀
+⠀⠀⠀⠀⠀⠀⠀⠙⠒⠒⠛⠛⠓⠉⢹⠀⣷⠴⣻⣽⡻⢧⢻⡿⡏⣼⢿⣻⢾⣿⣿⣿⡿⢠ ⠀⠀⠀⠀
+⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⠂⠻⠨⠰⢋⡅⠉⣑⡇⡗⣿⢂⣸⡿⣿⣛⠿⠃⠁ ⠀⠀⠀⠀
+⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠳⣌⣙⣸⢧⣿⣕⣼⣇⢹⠀⠀⠀⠀⠀⠀⠀⠀⠀
+⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣠⣸⢧⢟⢟⡟⣾⠀⠀⠀⠀⠀⠀⠀⠀⠀
+⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⢰⠙⣾⡟⣻⡕⣹⠀⠀⠀⠀⠀⠀⠀⠀⠀
+⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⢸⢰⡏⢠⡿⠾⠋⠀⠀⠀⠀⠀⠀⠀⠀⠀
+⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⢸⠸⡇⡏⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
+⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠸⢸⢸⡇⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
+⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠘⠇⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
+</pre>
+                        </div>
+                        <p>
+                            Dans-PersonalityEngine is a versatile model series
+                            fine-tuned on 50+ specialized datasets, designed to
+                            excel at both creative tasks (like roleplay and
+                            co-writing) and technical challenges (such as code
+                            generation, tool use, and complex reasoning).
+                        </p>
+                        <p>
+                            V1.3.0 introduces multilingual capabilities with
+                            support for 10 languages and enhanced domain
+                            expertise across multiple fields. The primary
+                            language is still English and that is where peak
+                            performance can be expected.
+                        </p>
+                        <h3>Multilingual Support</h3>
+                        <pre class="code-block">
+Arabic  Chinese   English  French      German
+Hindi   Japanese  Korean   Portuguese  Spanish</pre>
+                        <h3>Key Details</h3>
+                        <pre class="code-block">
+BASE MODEL: mistralai/Mistral-Small-3.1-24B-Base-2503
+LICENSE: apache-2.0
+LANGUAGE: Multilingual with 10 supported languages
+CONTEXT LENGTH: 32768 tokens, 131072 with degraded recall</pre>
+                        <h3>Recommended Settings</h3>
+                        <pre class="code-block">
+TEMPERATURE: 1.0
+TOP_P: 0.9</pre>
+                        <h3>Prompting Format</h3>
+                        <p>
+                            The model uses the following format I'll refer to as
+                            "DanChat-2":
+                        </p>
+                        <pre class="code-block">
+<|system|>system prompt<|endoftext|><|user|>Hi there!<|endoftext|><|assistant|>Hey, how can I help?<|endoftext|></pre>
+                        <h3>Why not ChatML?</h3>
+                        <p>
+                            While ChatML is a standard format for LLMs, it has
+                            limitations. DanChat-2 uses special tokens
+                            for each role, this reduces biases and helps the model adapt to different tasks more readily.
+                        </p>
+                        <h3>SillyTavern Template</h3>
+                        <p>
+                            <a
+                                href="https://huggingface.co/PocketDoc/Dans-PersonalityEngine-V1.3.0-24b/resolve/main/resources/DanChat-2.json?download=true"
+                                download
+                                target="_blank"
+                                rel="noopener noreferrer"
+                            >
+                                Download Master JSON
+                            </a>
+                        </p>
+                        <h3>Inference Provider</h3>
+                        <p>
+                            This model and others are available from ⚡Mancer AI for
+                            those interested in high quality inference without
+                            owning or renting expensive hardware.
+                        </p>
+                        <p class="mancer-button-container">
+                            <a
+                                href="https://mancer.tech/"
+                                target="_blank"
+                                rel="noopener noreferrer"
+                                class="mancer-button"
+                            >
+                                <span class="mancer-text">mancer</span>
+                            </a>
+                        </p>
+                        <h3>Training Process</h3>
+                        <p>
+                            The model was trained using Axolotl on 8x H100 GPUs
+                            for 50 hours. The resources to train this model were provided by Prime Intellect and Kalomaze.
+                        </p>
+                        <h3>Support Development</h3>
+                        <p>
+                            Development is limited by funding and resources. To
+                            help support:
+                        </p>
+                        <p>- Contact on HF</p>
+                        <p>- Email: [email protected]</p>
+                        <p class="coffee-container">
+                            <a
+                                href="https://www.buymeacoffee.com/visually"
+                                target="_blank"
+                                rel="noopener noreferrer"
+                            >
+                                <img
+                                    src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png"
+                                    alt="Buy Me A Coffee"
+                                    height="45"
+                                    width="162"
+                                />
+                            </a>
+                        </p>
+                    </div>
+                </div>
+            </div>
+        </div>
+    </div>
+    <style>
+        @import url("https://fonts.googleapis.com/css2?family=Consolas&display=swap");
+        .crt-container {
+            padding: 10px;
+            max-width: 1000px;
+            margin: 0 auto;
+            width: 95%;
+        }
+        .crt-case {
+            background: #e8d7c3;
+            border-radius: 10px;
+            padding: 15px;
+            box-shadow:
+                inset -2px -2px 5px rgba(0, 0, 0, 0.3),
+                2px 2px 5px rgba(0, 0, 0, 0.2);
+        }
+        .crt-inner-case {
+            background: #e8d7c3;
+            border-radius: 8px;
+            padding: 3px;
+            box-shadow:
+                inset -1px -1px 4px rgba(0, 0, 0, 0.3),
+                1px 1px 4px rgba(0, 0, 0, 0.2);
+        }
+        .crt-bezel {
+            background: linear-gradient(145deg, #1a1a1a, #2a2a2a);
+            padding: 15px;
+            border-radius: 5px;
+            border: 3px solid #0a0a0a;
+            position: relative;
+            box-shadow:
+                inset 0 0 20px rgba(0, 0, 0, 0.5),
+                inset 0 0 4px rgba(0, 0, 0, 0.4),
+                inset 2px 2px 4px rgba(255, 255, 255, 0.05),
+                inset -2px -2px 4px rgba(0, 0, 0, 0.8),
+                0 0 2px rgba(0, 0, 0, 0.6),
+                -1px -1px 4px rgba(255, 255, 255, 0.1),
+                1px 1px 4px rgba(0, 0, 0, 0.3);
+        }
+        .crt-bezel::before {
+            content: "";
+            position: absolute;
+            top: 0;
+            left: 0;
+            right: 0;
+            bottom: 0;
+            background: linear-gradient(
+                45deg,
+                rgba(255, 255, 255, 0.03) 0%,
+                rgba(255, 255, 255, 0) 40%,
+                rgba(0, 0, 0, 0.1) 60%,
+                rgba(0, 0, 0, 0.2) 100%
+            );
+            border-radius: 3px;
+            pointer-events: none;
+        }
+        .terminal-screen {
+            background: #111112;
+            padding: 20px;
+            border-radius: 15px;
+            position: relative;
+            overflow: hidden;
+            font-family: "Consolas", monospace;
+            font-size: clamp(12px, 1.5vw, 16px);
+            color: #e49b3e;
+            line-height: 1.4;
+            text-shadow: 0 0 2px #e49b3e;
+            /* Removed animation: flicker 0.15s infinite; */
+            filter: brightness(1.1) contrast(1.1);
+            box-shadow:
+                inset 0 0 30px rgba(0, 0, 0, 0.9),
+                inset 0 0 8px rgba(0, 0, 0, 0.8),
+                0 0 5px rgba(0, 0, 0, 0.6);
+            max-width: 80ch;
+            margin: 0 auto;
+        }
+        .terminal-screen h2,
+        .terminal-screen h3 {
+            font-size: clamp(16px, 2vw, 20px);
+            margin-bottom: 1em;
+            color: #e49b3e;
+        }
+        .terminal-screen pre.code-block {
+            font-size: clamp(10px, 1.3vw, 14px);
+            white-space: pre; /* Changed from pre-wrap to pre */
+            margin: 1em 0;
+            background-color: #1a1a1a;
+            padding: 1em;
+            border-radius: 4px;
+            color: #e49b3e;
+            overflow-x: auto; /* Added to enable horizontal scrolling */
+        }
+        .terminal-screen::before {
+            content: "";
+            position: absolute;
+            top: 0;
+            left: 0;
+            right: 0;
+            bottom: 0;
+            background:
+                linear-gradient(
+                    rgba(18, 16, 16, 0) 50%,
+                    rgba(0, 0, 0, 0.25) 50%
+                ),
+                url("data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADIAAAAyBAMAAADsEZWCAAAAGFBMVEUAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA4o8JoAAAAB3RSTlMAGwQIEQMYADcPzwAAACJJREFUKM9jYBgFo2AU0Beg+A8YMCLxGYZCbNQEo4BaAAD5TQiR5wU9vAAAAABJRU5ErkJggg==");
+            background-size: 100% 2.5px;
+            /* Removed animation: scan 1s linear infinite; */
+            pointer-events: none;
+            z-index: 2;
+        }
+        .terminal-screen::after {
+            content: "";
+            position: absolute;
+            top: 0;
+            left: 0;
+            right: 0;
+            bottom: 0;
+            background: radial-gradient(
+                circle at center,
+                rgba(17, 17, 18, 0) 0%,
+                rgba(17, 17, 18, 0.2) 50%,
+                rgba(17, 17, 18, 0.15) 100%
+            );
+            border-radius: 20px;
+            /* Removed animation: vignette-pulse 3s infinite; */
+            pointer-events: none;
+            z-index: 1;
+        }
+        .terminal-screen details {
+            margin: 1em 0;
+            padding: 0.5em;
+            border: 1px solid #e49b3e;
+            border-radius: 4px;
+        }
+        .terminal-screen summary {
+            cursor: pointer;
+            font-weight: bold;
+            margin: -0.5em;
+            padding: 0.5em;
+            border-bottom: 1px solid #e49b3e;
+            color: #e49b3e;
+        }
+        .terminal-screen details[open] summary {
+            margin-bottom: 0.5em;
+        }
+        .badge-container,
+        .coffee-container {
+            text-align: center;
+            margin: 1em 0;
+        }
+        .badge-container img,
+        .coffee-container img {
+            max-width: 100%;
+            height: auto;
+        }
+        .terminal-screen a {
+            color: #e49b3e;
+            text-decoration: underline;
+            transition: opacity 0.2s;
+        }
+        .terminal-screen a:hover {
+            opacity: 0.8;
+        }
+        .terminal-screen strong,
+        .terminal-screen em {
+            color: #f0f0f0; /* off-white color for user/system messages */
+        }
+        .terminal-screen p {
+            color: #f0f0f0; /* off-white color for assistant responses */
+        }
+        .terminal-screen p,
+        .terminal-screen li {
+            color: #e49b3e;
+        }
+        .terminal-screen code,
+        .terminal-screen kbd,
+        .terminal-screen samp {
+            color: #e49b3e;
+            font-family: "Consolas", monospace;
+            text-shadow: 0 0 2px #e49b3e;
+            background-color: #1a1a1a;
+            padding: 0.2em 0.4em;
+            border-radius: 4px;
+        }
+        .terminal-screen pre.code-block,
+        .terminal-screen pre {
+            font-size: clamp(10px, 1.3vw, 14px);
+            white-space: pre; /* Changed from pre-wrap to pre */
+            margin: 1em 0;
+            background-color: #1a1a1a;
+            padding: 1em;
+            border-radius: 4px;
+            color: #e49b3e;
+            overflow-x: auto; /* Added to enable horizontal scrolling */
+        }
+        .mancer-button-container {
+            text-align: left;
+            margin: 1em 0;
+        }
+        .mancer-button {
+            display: inline-flex;
+            align-items: center;
+            gap: 8px;
+            background: #1a1a1a;
+            color: #e49b3e;
+            padding: 15px 15px;
+            border: 2px solid #e49b3e;
+            border-radius: 5px;
+            text-decoration: none !important;
+            box-shadow: 0 0 10px rgba(228, 155, 62, 0.3);
+            transition: all 0.3s ease;
+            position: relative;
+        }
+        .mancer-text {
+            font-family: "Consolas", monospace;
+            font-weight: bold;
+            font-size: 20px;
+            text-shadow: 0 0 2px #e49b3e;
+            line-height: 1;
+            display: inline-block;
+            margin-left: -4px;
+            margin-top: -2px;
+        }
+        .mancer-button::before {
+            content: "⚡";
+            display: inline-flex;
+            align-items: center;
+            justify-content: center;
+            font-size: 20px;
+            line-height: 1;
+        }
+        .mancer-button:hover {
+            background: #2a2a2a;
+            box-shadow: 0 0 15px rgba(228, 155, 62, 0.5);
+            text-shadow: 0 0 4px #e49b3e;
+            text-decoration: none !important;
+        }
+    </style>
+</html>