README.md

metadata

base_model: zai-org/AutoGLM-Phone-9B-Multilingual
library_name: gguf
license: other
license_name: glm-4
tags:
  - gguf
  - llama.cpp
  - vision
  - multimodal
  - autoglm
  - phone-agent
  - android
  - gui-agent
pipeline_tag: text-generation

AutoGLM-Phone-9B-Multilingual (GGUF Quantizations)

This is a GGUF quantized version of zai-org/AutoGLM-Phone-9B-Multilingual, optimized for local inference with llama.cpp.

Includes vision encoder (mmproj) for multimodal capabilities and GUI agent tasks.

📦 Model Files

File	Quantization	Size	VRAM	Description
AutoGLM-Phone-9B-Multilingual-q4_k_m.gguf	Q4_K_M	5.7G	~10GB	Performance balanced
AutoGLM-Phone-9B-Multilingual-q5_k_m.gguf	Q5_K_M	6.6G	~11GB	High quality
AutoGLM-Phone-9B-Multilingual-q6_k.gguf	Q6_K	7.7G	~12GB	Excellent quality
AutoGLM-Phone-9B-Multilingual-q8_0.gguf	Q8_0	9.4G	~14GB	Best quality
mmproj-AutoGLM-Phone-9B-Multilingual-F16.gguf	F16	1.7G	-	Vision Encoder (required)

Total storage: ~31GB (all quantizations + vision encoder)

🚀 Quick Start

1. Install llama.cpp

git clone https://github.com/ggml-org/llama.cpp
cd llama.cpp
make llama-server

2. Download Model

huggingface-cli download gannima/AutoGLM-Phone-9B-Multilingual-GGUF \
    --local-dir ./AutoGLM-Phone-9B-Multilingual \
    --local-dir-use-symlinks False

3. Run Server

./llama-server \
    -m AutoGLM-Phone-9B-Multilingual/AutoGLM-Phone-9B-Multilingual-q8_0.gguf \
    --mmproj AutoGLM-Phone-9B-Multilingual/mmproj-AutoGLM-Phone-9B-Multilingual-F16.gguf \
    -c 32768 \
    -ngl 99 \
    --flash-attn on \
    --host 0.0.0.0 \
    --port 8080

4. Use with Open-AutoGLM

cd Open-AutoGLM
python main.py \
    --base-url http://localhost:8080 \
    --model "AutoGLM-Phone-9B-Multilingual" \
    --apikey dummy \
    "打开设置应用" \
    --max-steps 20

💻 Hardware Requirements

Quick Reference (Tested on RTX 4090)

Quantization	Model Size	Vision Encoder	Total	Actual VRAM*	Quality
Q4_K_M	5.7G	1.7G	~7.4G	~10GB	Good
Q5_K_M	6.6G	1.7G	~8.3G	~11GB	Very Good
Q6_K	7.7G	1.7G	~9.4G	~12GB	Excellent
Q8_0	9.4G	1.7G	~11.1G	~14GB	Best

*VRAM usage measured with --flash-attn on and all layers on GPU (-ngl 99)

System Requirements

• OS: Linux (Ubuntu 22.04+ recommended), Windows 11 with WSL2
• RAM: 32GB+ system memory recommended
• Storage: SSD with sufficient space for model files
• CUDA: 12.0+ for GPU acceleration
• llama.cpp: Latest version with GLM4V support (PR #18042 merged)

Performance Notes

• Flash Attention: Enabled by default for better performance
• KV Cache: Quantized to Q8_0 to reduce memory usage
• Batch Size: Optimized for RTX 4090 (adjust based on your GPU)
• Context: Supports up to 32K tokens with M-RoPE
• All layers on GPU: Set -ngl 99 to offload all transformer layers to GPU

🎯 Recommended Usage

For GUI Agent Tasks (Recommended)

Use Q5_K_M or Q6_K for the best balance between quality and performance:

• Better reasoning accuracy
• Faster inference than Q8_0
• Lower VRAM usage

For Maximum Quality

Use Q8_0 when:

• You want the highest possible accuracy
• Running on RTX 4090 or better
• Complex multi-step GUI automation tasks

For Consumer GPUs

Use Q4_K_M when:

• Limited VRAM (12GB cards like RTX 4070)
• Need faster inference
• Running on gaming GPUs

📄 License

This model is governed by the GLM-4 License. Please refer to the original model repository for details: https://huggingface.co/zai-org/AutoGLM-Phone-9B-Multilingual

🙏 Acknowledgments

• Original Model: zai-org/AutoGLM-Phone-9B-Multilingual
• Conversion Tool: llama.cpp
• GLM4V Support: PR #18042

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Conversion Date: 2025-12-29 llama.cpp Version: latest (with GLM4V support) Tested Hardware: RTX 4090 24GB