Dans PersonalityEngine V1.3.0 24b GGUF

🚀 Dans-PersonalityEngine-V1.3.0-24b GGUF Models

Dans-PersonalityEngine-V1.3.0-24b is a versatile model series fine - tuned on 50+ specialized datasets. It excels at both creative tasks like role - play and co - writing, and technical challenges such as code generation, tool use, and complex reasoning. V1.3.0 introduces multilingual capabilities with support for 10 languages and enhanced domain expertise across multiple fields.

✨ Features

• Multilingual Support: Supports languages including English, Arabic, German, French, Spanish, Hindi, Portuguese, Japanese, and Korean.
• Fine - Tuned on Diverse Datasets: Trained on a wide range of datasets covering various domains like role - play, math, task - solving, and more.
• Optimized Quantization: Utilizes advanced quantization methods for better memory efficiency and performance.

📚 Documentation

Model Generation Details

This model was generated using llama.cpp at commit f5cd27b7.

Ultra - Low - Bit Quantization with IQ - DynamicGate (1 - 2 bit)

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 were conducted on Llama - 3 - 8B - Instruct using:

• Standard perplexity evaluation pipeline
• 2048 - token context window
• The 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% compared to standard 1 - 2bit

Quantization Performance Comparison (Llama - 3 - 8B)

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 a 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 format with high precision but a smaller 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 low - memory environments.

Summary Table: Model Format Selection

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.

If you find these models useful

• 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

How to test: Choose an AI assistant type:

• TurboLLM (GPT - 4o - mini)
• HugLLM (Huggingface 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
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  • Security Audits
  • Penetration testing (Nmap/Metasploit)
  • Get more tokens by logging in or downloading our Free Network Monitor Agent with integrated AI Assistant

• 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!

Key Details