Qwen3 14B GGUF

🚀 Qwen3-14B GGUF Models

This project provides Qwen3-14B GGUF models, which are optimized for different hardware and memory requirements. These models offer various quantization methods and formats to meet diverse application scenarios, such as high-performance inference, memory-constrained deployments, and research on ultra-low-bit quantization.

🚀 Quick Start

The Qwen3-14B GGUF models can be used based on your specific hardware and memory conditions. First, select the appropriate model format according to your device's capabilities and requirements. Then, follow the corresponding usage instructions for inference or further processing.

✨ Features

Model Generation Details

This model was generated using llama.cpp at commit 19e899c.

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

• Precision-adaptive quantization: Our latest quantization method introduces precision-adaptive quantization for ultra-low-bit models (1-2 bit), with benchmark-proven improvements on Llama-3-8B.
• Layer-specific strategies: 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 a standard perplexity evaluation pipeline, a 2048-token context window, and the same prompt set across all quantizations.
• Quantization performance comparison: The results show significant improvements in perplexity and comparable inference speeds for different quantization variants.

Choosing the Right Model Format

• BF16: A 16-bit floating-point format designed for faster computation while retaining good precision. Recommended if your hardware supports BF16 acceleration.
• F16: A 16-bit floating-point format with high precision and wider device support. Suitable for devices with FP16 acceleration.
• Quantized models: Various quantized models, such as Q4_K, Q6_K, Q8_0, etc., offer different trade-offs between precision and memory usage, suitable for different hardware and memory constraints.

Included Files & Details

The package includes multiple model files in different formats, such as BF16, F16, and various quantized formats, to meet different usage scenarios and device requirements.

📚 Documentation

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

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)

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

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

Included Files & Details

Qwen3-14B-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.

Qwen3-14B-f16.gguf

• Model weights stored in F16.
• Use if your device supports FP16, especially if BF16 is not available.

Qwen3-14B-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.

Qwen3-14B-f16-q8_0.gguf

• Output & embeddings remain in F16.
• All other layers quantized to Q8_0.

Qwen3-14B-q4_k.gguf

• Output & embeddings quantized to Q8_0.
• All other layers quantized to Q4_K.
• Good for CPU inference with limited memory.

Qwen3-14B-q4_k_s.gguf

• Smallest Q4_K variant, using less memory at the cost of accuracy.
• Best for very low-memory setups.

Qwen3-14B-q6_k.gguf

• Output & embeddings quantized to Q8_0.
• All other layers quantized to Q6_K.

Qwen3-14B-q8_0.gguf

• Fully Q8 quantized model for better accuracy.
• Requires more memory but offers higher precision.

Qwen3-14B-iq3_xs.gguf

• IQ3_XS quantization, optimized for extreme memory efficiency.
• Best for ultra-low-memory devices.

Qwen3-14B-iq3_m.gguf

• IQ3_M quantization, offering a medium block size for better accuracy.
• Suitable for low-memory devices.

Qwen3-14B-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

1. Click the chat icon (bottom right on any page)
2. 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!

Other Assistants

• TurboLLM – Uses gpt-4-mini for:
  • Real-time network diagnostics
  • Automated penetration testing (Nmap/Metasploit)
  • Get more tokens by downloading our Free Network Monitor Agent
• HugLLM – Open-source models (≈8B params):
  • 2x more tokens than TurboLLM
  • AI-powered log analysis
  • Runs on Hugging Face Inference API

Example AI Commands to 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 quick Nmap vulnerability test"

Qwen3 Highlights

Qwen3 is the latest generation of large language models in the Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features:

• Uniquely support of seamless switching between thinking mode (for complex logical reasoning, math, and coding) and non-thinking mode (for efficient, general-purpose dialogue) within a single model, ensuring optimal performance across various scenarios.
• Significantly enhanced reasoning capabilities, surpassing previous QwQ (in thinking mode) and Qwen2.5 instruct models (in non-thinking mode) on mathematics, code generation, and commonsense logical reasoning.
• Superior human preference alignment, excelling in creative writing, role-playing, multi-turn dialogues, and instruction following, to deliver a more natural, engaging, and immersive conversational experience.
• Expertise in agent capabilities, enabling precise integration with external tools in both thinking and unthinking modes and achieving leading performance among open-source models in complex agent-based tasks.
• Support of 100+ languages and dialects with strong performance.

📄 License

This project is licensed under the Apache-2.0 License.