GLM 4 32B 0414 GGUF

🚀 GLM-4-32B-0414 GGUF Models

GLM-4-32B-0414 GGUF models are a series of models with 32 billion parameters. Their performance is comparable to OpenAI’s GPT series and DeepSeek’s V3/R1 series, and they support user - friendly local deployment.

✨ Features

Model Generation Details

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

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

Our latest quantization method introduces precision - adaptive quantization for ultra - low - bit models (1 - 2 bit), with benchmark - proven improvements on Llama - 3 - 8B. 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:

• 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% 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-4-32B-0414-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.

GLM-4-32B-0414-f16.gguf

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

GLM-4-32B-0414-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.

GLM-4-32B-0414-f16-q8_0.gguf

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

GLM-4-32B-0414-q4_k.gguf

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

GLM-4-32B-0414-q4_k_s.gguf

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

GLM-4-32B-0414-q6_k.gguf

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

GLM-4-32B-0414-q8_0.gguf

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

GLM-4-32B-0414-iq3_xs.gguf

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

GLM-4-32B-0414-iq3_m.gguf

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

GLM-4-32B-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: 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 Free Network Monitor Agents
• Real - time network diagnostics and monitoring
• Security Audits
• Penetration testing (Nmap/Metasploit)
• 🔑 Get more tokens by logging in or downloading our Free Network Monitor Agent with integrated AI Assistant

🔵 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 Free Network Monitor Agent to run the .net code from. This is a very flexible and powerful feature. Use with caution!

📚 Documentation

Introduction

The GLM family welcomes new members, the GLM - 4 - 32B - 0414 series models, featuring 32 billion parameters. Its performance is comparable to OpenAI’s GPT series and DeepSeek’s V3/R1 series. It also supports very user - friendly local deployment features. GLM - 4 - 32B - Base - 0414 was pre - trained on 15T of high - quality data, including substantial reasoning - type synthetic data. This lays the foundation for subsequent reinforcement learning extensions. In the post - training stage, we employed human preference alignment for dialogue scenarios. Additionally, using techniques like rejection sampling and reinforcement learning, we enhanced the model’s performance in instruction following, engineering code, and function calling, thus strengthening the atomic capabilities required for agent tasks.

GLM - Z1 - 32B - 0414 is a reasoning model with deep thinking capabilities. This was developed based on GLM - 4 - 32B - 0414 through cold start, extended reinforcement learning, and further training on tasks including 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 training, we also introduced general reinforcement learning based on pairwise ranking feedback, which enhances the model's general capabilities.

GLM - Z1 - Rumination - 32B - 0414 is a deep reasoning model with rumination capabilities (against OpenAI's Deep Research). Unlike typical deep thinking models, the rumination model is capable of deeper and longer thinking to solve more open - ended and complex problems (e.g., writing a comparative analysis of AI development in two cities and their future development plans). Z1 - Rumination is trained through scaling end - to - end reinforcement learning with responses graded by the ground truth answers or rubrics and can make use of search tools during its deep thinking process to handle complex tasks. The model shows significant improvements in research - style writing and complex tasks.

Finally, GLM - Z1 - 9B - 0414 is a surprise. We employed all the aforementioned techniques to train a small model (9B). GLM - Z1 - 9B - 0414 exhibits excellent capabilities in mathematical reasoning and general tasks. Its overall performance is top - ranked among all open - source models of the same size. Especially in resource - constrained scenarios, this model achieves an excellent balance between efficiency and effectiveness, providing a powerful option for users seeking lightweight deployment.

Showcase

Animation Generation

Web Design

Model	Description	Media
GLM - 4 - 32B - 0414	Design a drawing board that supports custom function plotting, allowing adding and deleting custom functions, and assigning colors to functions. (Prompt translated from Chinese)
GLM - 4 - 32B - 0414	Design a UI for a mobile machine learning platform, which should include interfaces for training tasks, storage management, and personal statistics. The personal statistics interface should use charts to display the user's resource usage over a period. Use Tailwind CSS to style the page, and display these 3 mobile interfaces tiled on a single HTML page. (Prompt translated from Chinese)

SVG Generation

Model	Description	Media
GLM - 4 - 32B - 0414	Create a misty Jiangnan scene using SVG. (Prompt translated from Chinese)
GLM - 4 - 32B - 0414	Use SVG to illustrate the training process of an LLM. (Prompt translated from Chinese)

Search - Based Writing

For search - based writing tasks, we use the following system prompt to have the model respond based on search results:

Please answer the user's question based on the given search results.

## Note
1. Make full use of and organize the collected information, rather than simply copying and pasting, to generate a professional answer that meets the user's requirements and is in - depth.
2. When sufficient information is provided, your answer should be as long as possible. Starting from the user's intention, provide a response with sufficient information and multiple perspectives.
3. Additionally, not all search results are closely related to the user's question. Please carefully甄别, filter, and utilize them.
4. The answers to objective - type Q&A are usually very short. You can appropriately add one or two sentences of relevant information to enrich the content.
5. Please ensure that your response has a beautiful format and is highly readable. For multi - entity comparisons or listings, make good use of the list format to help users better understand the information.
6. Unless the user requests otherwise, please keep the language of your answer consistent with the language of the user's question.
7. In appropriate cases, use the format such as:【0†source】at the end of the sentence to reference the search results.

When using, you can obtain search results through methods such as RAG or WebSearch, and wrap them in observation, for example:

[
    {
        "role": "user",
        "content": "Explore the common characteristics of children's literature, with a focus on its narrative techniques and thematic tendencies. This includes narrative techniques: common approaches in children's literature such as first - person, third - person, omniscient narrator, and interactive narration, and their influence on young readers. It also includes thematic tendencies: recurring themes in children's literature such as growth, adventure, friendship, and family, with an analysis of how these themes impact children's cognitive and emotional development. Additionally, other universal features such as the use of personification, repetitive language, symbolism and metaphor, and educational value should be considered. Please provide a detailed analytical report based on academic research, classic examples of children's literature, and expert o

📄 License

The model is released under the MIT license.