GLM Z1 32B 0414 GGUF

🚀 GLM-Z1-32B-0414 GGUF Models

This project presents the GLM-Z1-32B-0414 GGUF models, which offer high - performance text generation capabilities. These models are suitable for various applications, especially in scenarios where memory efficiency and accurate text generation are crucial.

🚀 Quick Start

The README provides detailed information about model generation, quantization methods, model format selection, and included files. You can quickly get an overview of how to choose the right model for your needs based on your hardware and memory constraints.

✨ Features

Model Generation Details

This model was generated using llama.cpp at commit e291450.

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: It 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.

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, which 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

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 a 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

GLM - Z1 - 32B - 0414 - bf16.gguf

• Model weights are preserved in BF16.
• Use this if you want to requantize the model into a different format.
• Best if your device supports BF16 acceleration.

GLM - Z1 - 32B - 0414 - f16.gguf

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

GLM - Z1 - 32B - 0414 - bf16 - q8_0.gguf

• Output & embeddings remain in BF16.
• All other layers are quantized to Q8_0.
• Use if your device supports BF16 and you want a quantized version.

GLM - Z1 - 32B - 0414 - f16 - q8_0.gguf

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

GLM - Z1 - 32B - 0414 - q4_k.gguf

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

GLM - Z1 - 32B - 0414 - q4_k_s.gguf

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

GLM - Z1 - 32B - 0414 - q6_k.gguf

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

GLM - Z1 - 32B - 0414 - q8_0.gguf

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

GLM - Z1 - 32B - 0414 - iq3_xs.gguf

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

GLM - Z1 - 32B - 0414 - iq3_m.gguf

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

GLM - Z1 - 32B - 0414 - 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"

GLM - 4 - Z1 - 32B - 0414

Introduction

The GLM family welcomes a new generation of open - source models, the GLM - 4 - 32B - 0414 series, featuring 32 billion parameters. Its performance is comparable to OpenAI's GPT series and DeepSeek's V3/R1 series, and it supports very user - friendly local deployment features. GLM - 4 - 32B - Base - 0414 was pre - trained on 15T of high - quality data, including a large amount of reasoning - type synthetic data, laying the foundation for subsequent reinforcement learning extensions. In the post - training stage, in addition to human preference alignment for dialogue scenarios, we also enhanced the model's performance in instruction following, engineering code, and function calling using techniques such as rejection sampling and reinforcement learning, strengthening the atomic capabilities required for agent tasks. GLM - 4 - 32B - 0414 achieves good results in areas such as engineering code, Artifact generation, function calling, search - based Q&A, and report generation. Some benchmarks even rival larger models like GPT - 4o and DeepSeek - V3 - 0324 (671B).

• GLM - Z1 - 32B - 0414 is a reasoning model with deep thinking capabilities. It was developed based on GLM - 4 - 32B - 0414 through cold start and extended reinforcement learning, as well as further training of the model on tasks involving mathematics, code, and logic. Compared to the base model, GLM - Z1 - 32B - 0414 significantly improves mathematical abilities and the capability to solve complex tasks. During the training process, we also introduced general reinforcement learning based on pairwise ranking feedback, further enhancing the model's general capabilities.
• GLM - Z1 - Rumination - 32B - 0414 is a deep reasoning model with rumination capabilities

License

This project is licensed under the MIT license.