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
PLM-1.8B-Instruct-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.
PLM-1.8B-Instruct-f16.gguf
Model weights stored in F16.
Use if your device supports FP16, especially if BF16 is not available.
PLM-1.8B-Instruct-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.
PLM-1.8B-Instruct-f16-q8_0.gguf
Output & embeddings remain in F16.
All other layers quantized to Q8_0.
PLM-1.8B-Instruct-q4_k.gguf
Output & embeddings quantized to Q8_0.
All other layers quantized to Q4_K.
Good for CPU inference with limited memory.
PLM-1.8B-Instruct-q4_k_s.gguf
Smallest Q4_K variant, using less memory at the cost of accuracy.
Best for very low-memory setups.
PLM-1.8B-Instruct-q6_k.gguf
Output & embeddings quantized to Q8_0.
All other layers quantized to Q6_K .
PLM-1.8B-Instruct-q8_0.gguf
Fully Q8 quantized model for better accuracy.
Requires more memory but offers higher precision.
PLM-1.8B-Instruct-iq3_xs.gguf
IQ3_XS quantization, optimized for extreme memory efficiency.
Best for ultra-low-memory devices.
PLM-1.8B-Instruct-iq3_m.gguf
IQ3_M quantization, offering a medium block size for better accuracy.
Suitable for low-memory devices.
PLM-1.8B-Instruct-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:
👉 Quantum Network Monitor
💬 How to test:
Click the chat icon (bottom right on any page)
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!
"Check if my server is using quantum safe encyption for communication"
"Run a quick Nmap vulnerability test"
'"Create a cmd processor to .. (what ever you want)" Note you need to install a Quantum Network Monitor Agent to run the .net code from. This is a very flexible and powerful feature. Use with caution!
Final word
I fund the servers to create the models files, run the Quantum Network Monitor Service and Pay for Inference from Novita and OpenAI all from my own pocket. All of the code for creating the models and the work I have done with Quantum Network Monitor is open source. Feel free to use what you find useful. Please support my work and consider buying me a coffee .
This will help me pay for the services and increase the token limits for everyone.
Thank you :)
plm-logo
🖲️ PLM: Efficient Peripheral Language Models Hardware-Co-Designed for Ubiquitous Computing
The PLM (Peripheral Language Model) series introduces a novel model architecture to peripheral computing by delivering powerful language capabilities within the constraints of resource-limited devices. Through modeling and system co-design strategy, PLM optimizes model performance and fits edge system requirements, PLM employs Multi-head Latent Attention and squared ReLU activation to achieve sparsity, significantly reducing memory footprint and computational demands. Coupled with a meticulously crafted training regimen using curated datasets and a Warmup-Stable-Decay-Constant learning rate scheduler, PLM demonstrates superior performance compared to existing small language models, all while maintaining the lowest activated parameters, making it ideally suited for deployment on diverse peripheral platforms like mobile phones and Raspberry Pis.
If you find Project PLM helpful for your research or applications, please cite as follows:
@misc{deng2025plmefficientperipherallanguage,
title={PLM: Efficient Peripheral Language Models Hardware-Co-Designed for Ubiquitous Computing},
author={Cheng Deng and Luoyang Sun and Jiwen Jiang and Yongcheng Zeng and Xinjian Wu and Wenxin Zhao and Qingfa Xiao and Jiachuan Wang and Lei Chen and Lionel M. Ni and Haifeng Zhang and Jun Wang},
year={2025},
eprint={2503.12167},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2503.12167},
}
