GLM Z1 9B 0414 GGUF

🚀 GLM-Z1-9B-0414 GGUF Models

This project offers GLM-Z1-9B-0414 GGUF models, which are generated using advanced quantization techniques. These models are suitable for various scenarios, especially those with memory constraints, and provide different formats to meet diverse hardware requirements.

✨ 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 proven improvements on Llama - 3 - 8B in benchmarks.
• Layer - Specific Strategies: It uses layer - specific strategies to preserve accuracy while maintaining extreme memory efficiency.
  • Dynamic Precision Allocation: The first/last 25% of layers use IQ4_XS (selected layers), and the middle 50% use IQ2_XXS/IQ3_S to increase efficiency.
  • Critical Component Protection: Embeddings/output layers use Q5_K, reducing error propagation by 38% compared to standard 1 - 2bit.
• Quantization Performance Comparison: In the comparison of Llama - 3 - 8B, our DynamicGate quantization shows significant improvements in perplexity with only modest size increases and comparable inference speeds.

Choosing the Right Model Format

• BF16 (Brain Float 16): Suitable for devices with BF16 acceleration, offering faster computation and reduced memory usage compared to FP32.
• F16 (Float 16): More widely supported than BF16, providing a balance between speed, memory usage, and accuracy.
• Quantized Models (Q4_K, Q6_K, Q8, etc.): Ideal for CPU and low - VRAM inference, with different levels of precision and memory usage.
• Very Low - Bit Quantization (IQ3_XS, IQ3_S, IQ3_M, Q4_K, Q4_0): Optimized for extreme memory efficiency, suitable for low - power devices and large - scale deployments.

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.

Testing the Models

• TestLLM: An experimental model with zero - configuration setup and no API costs, suitable for edge - device AI research.
• Other Assistants: TurboLLM uses gpt - 4 - mini for real - time network diagnostics, and HugLLM is based on open - source models for AI - powered log analysis.

📦 Installation

No installation steps are provided in the original document.

💻 Usage Examples

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"

📚 Documentation

GLM - 4 - Z1 - 9B - 0414 Introduction

The GLM family's new generation of open - source models, the GLM - 4 - 32B - 0414 series, features 32 billion parameters. It has comparable performance to OpenAI's GPT series and DeepSeek's V3/R1 series and supports user - friendly local deployment. GLM - 4 - 32B - Base - 0414 was pre - trained on 15T of high - quality data, and further enhanced in the post - training stage. GLM - Z1 - 32B - 0414 is a reasoning model with improved mathematical and complex task - solving abilities, and GLM - Z1 - Rumination - 32B - 0414 is a deep reasoning model with rumination capabilities.

🔧 Technical Details

Ultra - Low - Bit Quantization Benchmark

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.

Model Training

GLM - 4 - 32B - Base - 0414 was pre - trained on 15T of high - quality data, including a large amount of reasoning - type synthetic data. In the post - training stage, techniques such as rejection sampling and reinforcement learning were used to enhance the model's performance in instruction following, engineering code, and function calling.

📄 License

The model is licensed under the MIT license.