Dans PersonalityEngine V1.3.0 24b GGUF
Model Overview
Model Features
Model Capabilities
Use Cases
đ 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.
đ 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 â IQ4_XS (selected layers)
- Middle 50% â 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)
Quantization | Standard PPL | DynamicGate PPL | Î PPL | Std Size | DG Size | Î 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)
- Î 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 â 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) â 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) â 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.) â For CPU & Low - VRAM Inference
Quantization reduces model size and memory usage while maintaining as much accuracy as possible.
- Lower - bit models (Q4_K) â Best for minimal memory usage, may have lower precision.
- Higher - bit models (Q6_K, Q8_0) â 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
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.
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:
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 â 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 â 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
-
HugLLM â Latest Open - source models:
- Runs on Hugging Face Inference API
Example commands to you could test:
"Give me info on my websites SSL certificate"
"Check if my server is using quantum safe encyption for communication"
"Run a comprehensive security audit on my server"
"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
Property | Details |
---|---|
Model Type | Dans-PersonalityEngine-V1.3.0-24b GGUF Models |
Base Model | mistralai/Mistral-Small-3.1-24B-Base-2503 |
License | apache-2.0 |
Language | English, Arabic, German, French, Spanish, Hindi, Portuguese, Japanese, Korean |
Pipeline Tag | text - generation |
Library Name | transformers |
Context Length | 32768 tokens, 131072 with degraded recall |
Recommended Temperature | 1.0 |
Recommended Top_P | 0.9 |
Prompting Format | < |

