Instructions to use aquaduck/GLM-4.7-Flash-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aquaduck/GLM-4.7-Flash-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="aquaduck/GLM-4.7-Flash-GGUF",
	filename="GLM-4.7-Flash-Q4_K_M-layers-0-24.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use aquaduck/GLM-4.7-Flash-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf aquaduck/GLM-4.7-Flash-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf aquaduck/GLM-4.7-Flash-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf aquaduck/GLM-4.7-Flash-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf aquaduck/GLM-4.7-Flash-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf aquaduck/GLM-4.7-Flash-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf aquaduck/GLM-4.7-Flash-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf aquaduck/GLM-4.7-Flash-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf aquaduck/GLM-4.7-Flash-GGUF:Q4_K_M

Use Docker

docker model run hf.co/aquaduck/GLM-4.7-Flash-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use aquaduck/GLM-4.7-Flash-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aquaduck/GLM-4.7-Flash-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aquaduck/GLM-4.7-Flash-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/aquaduck/GLM-4.7-Flash-GGUF:Q4_K_M

Ollama
How to use aquaduck/GLM-4.7-Flash-GGUF with Ollama:
```
ollama run hf.co/aquaduck/GLM-4.7-Flash-GGUF:Q4_K_M
```

Unsloth Studio

How to use aquaduck/GLM-4.7-Flash-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for aquaduck/GLM-4.7-Flash-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for aquaduck/GLM-4.7-Flash-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for aquaduck/GLM-4.7-Flash-GGUF to start chatting

How to use aquaduck/GLM-4.7-Flash-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf aquaduck/GLM-4.7-Flash-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "aquaduck/GLM-4.7-Flash-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use aquaduck/GLM-4.7-Flash-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf aquaduck/GLM-4.7-Flash-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default aquaduck/GLM-4.7-Flash-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use aquaduck/GLM-4.7-Flash-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf aquaduck/GLM-4.7-Flash-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "aquaduck/GLM-4.7-Flash-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use aquaduck/GLM-4.7-Flash-GGUF with Docker Model Runner:
```
docker model run hf.co/aquaduck/GLM-4.7-Flash-GGUF:Q4_K_M
```

Lemonade

How to use aquaduck/GLM-4.7-Flash-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull aquaduck/GLM-4.7-Flash-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.GLM-4.7-Flash-GGUF-Q4_K_M

List all available models

lemonade list

Model Card for aquaduck/GLM-4.7-Flash-GGUF

Layer-sharded Q4_K_M GGUF chunks of Z.ai GLM-4.7-Flash, derived from unsloth/GLM-4.7-Flash-GGUF.

These files are not a new quantization. They are contiguous 4-layer packages (final chunk is 3 layers) cut from the full Unsloth Q4_K_M GGUF for staged / multi-node loading (Aquaduck Arc layer-package-v1).

Model lineage

zai-org/GLM-4.7-Flash └── quantized → unsloth/GLM-4.7-Flash-GGUF (Q4_K_M) └── layer-split → aquaduck/GLM-4.7-Flash-GGUF (this repo)

Base weights: https://huggingface.co/zai-org/GLM-4.7-Flash (MIT)
Quantization source: https://huggingface.co/unsloth/GLM-4.7-Flash-GGUF (tag Q4_K_M)
This repo: 4-layer GGUF shards for distributed inference

Model Details

Model Description

Developed by: Aquaduck (layer packaging only; base model by Z.ai / GLM Team; GGUF quant by Unsloth / llama.cpp ecosystem)
Shared by: Aquaduck AI
Model type: Causal language model (GLM-4 MoE Lite / 30B-A3B), GGUF Q4_K_M, layer-sharded
Language(s): Multilingual (same as base; primarily English and Chinese)
License: MIT (inherits from zai-org/GLM-4.7-Flash)
Finetuned from model: N/A — not a fine-tune
Derived from: unsloth/GLM-4.7-Flash-GGUF ← zai-org/GLM-4.7-Flash

Model Sources

Base model card / paper: https://huggingface.co/zai-org/GLM-4.7-Flash , https://arxiv.org/abs/2508.06471
Quantized GGUF source: https://huggingface.co/unsloth/GLM-4.7-Flash-GGUF

Files

File	Layers (inclusive start … end exclusive in filename)	Approx. size
GLM-4.7-Flash-Q4_K_M-layers-0-24.gguf	0–23	~9.10 GB
GLM-4.7-Flash-Q4_K_M-layers-24-47.gguf	24–46	~9.40 GB

Total layers: 47
Valid split boundaries: 24
Full-model equivalent: ~18.3 GB (same as Unsloth Q4_K_M)

Filenames use exclusive end indices (layers-{start}-{endExclusive}). The second half covers 23 layers (24–46).

Uses

Direct Use

Intended for Aquaduck / Arc (and compatible) runtimes that download and merge layer packages, not as a single drop-in file for stock llama.cpp.

For a normal single-file local run, use unsloth/GLM-4.7-Flash-GGUF or zai-org/GLM-4.7-Flash instead.

Use the base model’s GLM chat template; other formats will not work correctly. Recommended sampling (from Z.ai / Unsloth): temperature 1.0, top-p 0.95 for general use; temperature 0.7, top-p 1.0 for tool-calling. If using llama.cpp-compatible stacks, disable repeat penalty (or set it to 1.0) and prefer --min-p 0.01.

Out-of-Scope Use

Expecting any one chunk to run as a complete model
Treating this repo as a new training run or re-quant
Uses prohibited by the MIT license or Z.ai’s model card guidance

Bias, Risks, and Limitations

Same capabilities, biases, and risks as zai-org/GLM-4.7-Flash. Q4_K_M quantization (from Unsloth) can degrade quality vs. the original BF16 / higher-precision releases. Layer sharding does not change weights beyond packaging.

Recommendations

Follow Z.ai’s GLM-4.7 docs for chat template, reasoning, and tool use. Prefer the full Unsloth GGUF when you do not need staged loading.

How to Get Started

These shards are meant to be loaded automatically by an Aquaduck worker.

Install / open the Aquaduck desktop app and sign in as a worker.
Connect so the app can fetch your device’s model assignment from the model catalog (zai-org/glm-4.7-flash).
If model shards do not automatically download: Use Get latest assigned model (under Settings). The worker will:
- download only the contiguous layer-chunk GGUFs for your assigned stage from this repo
- merge multi-chunk stages into one stage GGUF
- keep that stage ready for split serving You do not need to pick individual *-layers-*.gguf files. Assignment and download are driven by model catalog metadata. This repo is not a single-file Q4_K_M drop-in for stock llama.cpp. For that, use https://huggingface.co/unsloth/GLM-4.7-Flash-GGUF instead.

Training Details

No training. Weights come from Z.ai / GLM Team; Q4_K_M GGUF from Unsloth; this repo only splits that GGUF into layer packages.

Evaluation

No separate evals for the shards. See zai-org/GLM-4.7-Flash and https://arxiv.org/abs/2508.06471.

Technical Specifications

Architecture: GLM-4 MoE Lite (Glm4MoeLiteForCausalLM), ~30B params / ~3B active
Quantization: Q4_K_M (Unsloth)
Packaging: Arc gguf-shard / catalog catalog:split → *-layers-{start}-{endExclusive}.gguf
Package format: layer-package-v1

Citation

@misc{5team2025glm45agenticreasoningcoding,
  title={GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models},
  author={GLM Team and Aohan Zeng and Xin Lv and Qinkai Zheng and Zhenyu Hou and Bin Chen and Chengxing Xie and Cunxiang Wang and Da Yin and Hao Zeng and Jiajie Zhang and Kedong Wang and Lucen Zhong and Mingdao Liu and Rui Lu and Shulin Cao and Xiaohan Zhang and Xuancheng Huang and Yao Wei and Yean Cheng and Yifan An and Yilin Niu and Yuanhao Wen and Yushi Bai and Zhengxiao Du and Zihan Wang and Zilin Zhu and Bohan Zhang and Bosi Wen and Bowen Wu and Bowen Xu and Can Huang and Casey Zhao and Changpeng Cai and Chao Yu and Chen Li and Chendi Ge and Chenghua Huang and Chenhui Zhang and Chenxi Xu and Chenzheng Zhu and Chuang Li and Congfeng Yin and Daoyan Lin and Dayong Yang and Dazhi Jiang and Ding Ai and Erle Zhu and Fei Wang and Gengzheng Pan and Guo Wang and Hailong Sun and Haitao Li and Haiyang Li and Haiyi Hu and Hanyu Zhang and Hao Peng and Hao Tai and Haoke Zhang and Haoran Wang and Haoyu Yang and He Liu and He Zhao and Hongwei Liu and Hongxi Yan and Huan Liu and Huilong Chen and Ji Li and Jiajing Zhao and Jiamin Ren and Jian Jiao and Jiani Zhao and Jianyang Yan and Jiaqi Wang and Jiayi Gui and Jiayue Zhao and Jie Liu and Jijie Li and Jing Li and Jing Lu and Jingsen Wang and Jingwei Yuan and Jingxuan Li and Jingzhao Du and Jinhua Du and Jinxin Liu and Junkai Zhi and Junli Gao and Ke Wang and Lekang Yang and Liang Xu and Lin Fan and Lindong Wu and Lintao Ding and Lu Wang and Man Zhang and Minghao Li and Minghuan Xu and Mingming Zhao and Mingshu Zhai and Pengfan Du and Qian Dong and Shangde Lei and Shangqing Tu and Shangtong Yang and Shaoyou Lu and Shijie Li and Shuang Li and Shuang-Li and Shuxun Yang and Sibo Yi and Tianshu Yu and Wei Tian and Weihan Wang and Wenbo Yu and Weng Lam Tam and Wenjie Liang and Wentao Liu and Xiao Wang and Xiaohan Jia and Xiaotao Gu and Xiaoying Ling and Xin Wang and Xing Fan and Xingru Pan and Xinyuan Zhang and Xinze Zhang and Xiuqing Fu and Xunkai Zhang and Yabo Xu and Yandong Wu and Yida Lu and Yidong Wang and Yilin Zhou and Yiming Pan and Ying Zhang and Yingli Wang and Yingru Li and Yinpei Su and Yipeng Geng and Yitong Zhu and Yongkun Yang and Yuhang Li and Yuhao Wu and Yujiang Li and Yunan Liu and Yunqing Wang and Yuntao Li and Yuxuan Zhang and Zezhen Liu and Zhen Yang and Zhengda Zhou and Zhongpei Qiao and Zhuoer Feng and Zhuorui Liu and Zichen Zhang and Zihan Wang and Zijun Yao and Zikang Wang and Ziqiang Liu and Ziwei Chai and Zixuan Li and Zuodong Zhao and Wenguang Chen and Jidong Zhai and Bin Xu and Minlie Huang and Hongning Wang and Juanzi Li and Yuxiao Dong and Jie Tang},
  year={2025},
  eprint={2508.06471},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  url={https://arxiv.org/abs/2508.06471},
}

Credit: