Qwen3 30B A6B 16 Extreme GGUF

🚀 Qwen3-30B-A6B-16-Extreme GGUF Models

This project offers Qwen3-30B-A6B-16-Extreme GGUF models, which are significant for text generation tasks. These models are generated with specific techniques and come in various quantization formats, catering to different hardware and memory requirements.

✨ Features

• Ultra-Low-Bit Quantization: Introduces precision - adaptive quantization for 1 - 2 bit ultra - low - bit models, proven effective on Llama - 3 - 8B.
• Multiple Model Formats: Provides different model formats like BF16, F16, and various quantized formats to suit diverse hardware capabilities and memory constraints.
• AI Network Monitoring: Comes with AI - powered network monitoring assistants for tasks such as network diagnostics, security audits, and penetration testing.

📦 Installation

No installation steps are provided in the original document.

💻 Usage Examples

No code examples are provided in the original document.

📚 Documentation

Model Generation Details

This model was generated using llama.cpp at commit 92ecdcc0.

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

Our latest quantization method brings precision - adaptive quantization for ultra - low - bit models (1 - 2 bit). Benchmark tests on Llama - 3 - 8B show improvements.

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 - 2 bit

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 - 2 bit 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 ARM - based devices or low - memory environments.

Summary Table: Model Format Selection

Included Files & Details

• Qwen3-30B-A6B-16-Extreme-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.
• Qwen3-30B-A6B-16-Extreme-f16.gguf: Model weights stored in F16. Use if your device supports FP16, especially if BF16 is not available.
• Qwen3-30B-A6B-16-Extreme-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.
• Qwen3-30B-A6B-16-Extreme-f16-q8_0.gguf: Output & embeddings remain in F16. All other layers quantized to Q8_0.
• Qwen3-30B-A6B-16-Extreme-q4_k.gguf: Output & embeddings quantized to Q8_0. All other layers quantized to Q4_K. Good for CPU inference with limited memory.
• Qwen3-30B-A6B-16-Extreme-q4_k_s.gguf: Smallest Q4_K variant, using less memory at the cost of accuracy. Best for very low - memory setups.
• Qwen3-30B-A6B-16-Extreme-q6_k.gguf: Output & embeddings quantized to Q8_0. All other layers quantized to Q6_K.
• Qwen3-30B-A6B-16-Extreme-q8_0.gguf: Fully Q8 quantized model for better accuracy. Requires more memory but offers higher precision.
• Qwen3-30B-A6B-16-Extreme-iq3_xs.gguf: IQ3_XS quantization, optimized for extreme memory efficiency. Best for ultra - low - memory devices.
• Qwen3-30B-A6B-16-Extreme-iq3_m.gguf: IQ3_M quantization, offering a medium block size for better accuracy. Suitable for low - memory devices.
• Qwen3-30B-A6B-16-Extreme-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

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 Quantum Network Monitor Agents
  • Real - time network diagnostics and monitoring
  • Security Audits
  • Penetration testing (Nmap/Metasploit)
• 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 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 used to create these model files, run the Quantum Network Monitor service, and pay for inference from Novita and OpenAI—all out of my own pocket. All the code behind the model creation and the Quantum Network Monitor project is open source. Feel free to use whatever you find helpful.

If you appreciate the work, please consider buying me a coffee ☕. Your support helps cover service costs and allows me to raise token limits for everyone.

I'm also open to job opportunities or sponsorship.

Thank you! 😊

Qwen3-30B-A6B-16-Extreme

This repo contains the full precision source code, in "safe tensors" format to generate GGUFs, GPTQ, EXL2, AWQ, HQQ and other formats. The source code can also be used directly.

This is a simple "finetune" of the Qwen's "Qwen 30B - A3B" (MOE) model, setting the experts in use from 8 to 16 (out of 128 experts).

TWO Example generations (Q4KS, CPU) at the bottom of this page using 16 experts / this model.

This reduces the speed of the model, but uses more "experts" to process your prompts and uses 6B (of 30B) parameters instead of 3B (of 30B) parameters. Depending on the application you may want to use the regular model ("30B - A3B"), and use this model for MORE COMPLEX / "DEEPER" (IE Nuance) use case(s).

For the 128k version, Neo Imatrix, with additional modified quants (IQ1M to Q8 Ultra (f16 experts)) - 52 quants - see: [ https://huggingface.co/DavidAU/Qwen3-128k-30B-A3B-NEO-MAX-Imatrix-gguf ]

Regular or simpler use cases may benefit from using the normal (8 experts), the "12 cooks" (12 experts) or "High - Speed" (4 experts) version(s).

Using 16 experts instead of the default 8 will slow down token/second speeds about about 1/2 or so.

Context size: 32K + 8K for output (40k total)

Use Jinja Template or CHATML template.

IMPORTANT NOTES

• Due to the unique nature (MOE, Size, Activated experts, size of experts) of this model GGUF quants can be run on the CPU, GPU or with GPU part "off - load", right up to full precision.
• This model is difficult to Imatrix: You need a much larger imatrix file / multi - language / multi - content (ie code/text) to imatrix it.
• GPU speeds will be BLISTERING 4x - 8x or higher than CPU only speeds AND this model will be BLISTERING too, relative to other "30B" models (Token per second speed equal roughly to 6B "normal" model speeds).

Please refer the org model card for details, benchmarks, how to use, settings, system roles etc etc: [ https://huggingface.co/Qwen/Qwen3-30B-A3B ]

More / Less Experts Versions

• 4 experts: [ https://huggingface.co/DavidAU/Qwen3-30B-A1.5B-High-Speed ]
• 12 experts: [ https://huggingface.co/DavidAU/Qwen3-30B-A4.5B-12-Cooks ]
• 16 experts, 128k context: [ https://huggingface.co/DavidAU/Qwen3-30B-A6B-16-Extreme-128k-context ]
• 24 experts: [ https://huggingface.co/DavidAU/Qwen3-30B-A7.5B-24-Grand-Brainstorm ]

OPTIONAL SYSTEM ROLE

You may or may not need this, as most times Qwen3s generate their own reasoning/thinking blocks.

You are a deep thinking AI, you may use extremely long chains of thought to deeply consider the problem and deliberate with yourself via systematic reasoning processes to help come to a correct solution prior to answering. You should enclose your thoughts and internal monologue inside <think> </think> tags, and then provide your solution or response to the problem.

See document "Maximizing - Model - Performance - All..." below for how to "set" system role in various LLM/AI apps below.

IMPORTANT: Highest Quality Settings / Optimal Operation Guide / Parameters and Samplers

If you are going to use this model, (source, GGUF or a different quant), please review this document for critical parameter, sampler and advance sampler settings (for multiple AI/LLM aps).

This a "Class 1" (settings will enhance operation) model:

For all settings used for this model (including specifics for its "class"), including example generation(s) and for advanced settings guide (which many times addresses any model issue(s)), including methods to improve model performance for all use case(s) as well as chat, roleplay and other use case(s) (especially for use case(s) beyond the model's design) please see: [ https://huggingface.co/DavidAU/Maximizing-Model-Performance-All-Quants-Types-And-Full-Precision-by-Samplers_Parameters ]

REASON:

Regardless of "model class" this document will detail methods to enhance operations.

If the model is a Class 3/4 model the default settings (parameters, samplers, advanced samplers) must be set for "use case(s)" uses correctly. Some AI/LLM apps DO NOT have consistant default setting(s) which result in sub - par model operation. Like wise for Class 3/4 models (which operate somewhat to very differently than standard models) additional samplers and advanced samplers settings are required to "smooth out" operation, AND/OR also allow full operation for use cases the model was not designed for.

BONUS - Use these settings for ANY model, ANY repo, ANY quant (including source/full precision):

This document also details parameters, sampler and advanced samplers that can be use FOR ANY MODEL, FROM ANY REPO too - all quants, and of course source code operation too - to enhance the operation of any model. [ https://huggingface.co/DavidAU/Maximizing-Model-Performance-All-Quants-Types-And-Full-Precision-by-Samplers_Parameters ]

NOTE:

I strongly suggest you also visit the DavidAU GGUF (below) repo too for more details in using this model; especially if it is "Class 3" or "Class 4" to get maximum performance from the model.

For full information about this model, including:

• Details about this model and its use case(s).
• Context limits
• Special usage notes / settings.
• Any model(s) used to create this model.
• Template(s) used to access/use this model.
• Example generation(s)
• GGUF quants of this model

Please go to: [ GGUFS REPO coming soon ]

Example Generation #1

Temp 1.2, rep pen 1.06, rep pen range 64, topk 100, topp .95, minp .05

Q4KS [non - imatrix], CPU only (windows 11), LMSTUDIO.

Speed: 11 T/S (no GPU offload)

This is a mid - level quant, expect stronger performance from an Imatrix Q4KS / higher quants / full precision.

NOTE: Some formatting lost on copy/paste.

PROMPT

Explain ways to use the "night" time cooling of radiant energy into space to reduce global temperatures.

[[[thinking start]]]

Thought for 2 minutes 17 seconds

Okay, so the user is asking about how to use the nighttime cooling of radiant energy into space to reduce global temperatures. Hmm, I need to think about this carefully. First, let's break down what they mean by "nighttime cooling of radiant energy into space." I guess that refers to the Earth emitting heat radiation during the night, which escapes into space. But how does that help in reducing global temperatures?

Wait, but isn't that natural? The Earth's atmosphere and surface emit thermal radiation, which is part of the planet's energy balance. During the day, solar radiation comes in, and at night, it radiates back. But maybe there's a way to enhance this process to cool down more effectively. But how?

I need to consider the greenhouse effect. If the Earth's atmosphere traps heat, then the cooling at night might be limited by that. So if we can reduce the greenhouse effect during nighttime, maybe more heat would escape into space. But how? Maybe reducing greenhouse gases? But that's a broad approach. However, the user is specifically asking about using nighttime cooling.

Another angle: Maybe reflecting more sunlight during the day to reduce heat absorption, but that's not nighttime. Alternatively, using materials that emit more infrared radiation at night. For example, certain surfaces that are good radiators of thermal energy. But how would that work?

Also, there's something called "nighttime cooling" in architecture, where buildings use materials that radiate heat to the sky.