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

Libraries

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

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ubergarm/GLM-4.7-GGUF",
	filename="IQ2_KL/GLM-4.7-IQ2_KL-00001-of-00004.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 ubergarm/GLM-4.7-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ubergarm/GLM-4.7-GGUF:Q2_K
# Run inference directly in the terminal:
llama-cli -hf ubergarm/GLM-4.7-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ubergarm/GLM-4.7-GGUF:Q2_K
# Run inference directly in the terminal:
llama-cli -hf ubergarm/GLM-4.7-GGUF:Q2_K

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 ubergarm/GLM-4.7-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf ubergarm/GLM-4.7-GGUF:Q2_K

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 ubergarm/GLM-4.7-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ubergarm/GLM-4.7-GGUF:Q2_K

Use Docker

docker model run hf.co/ubergarm/GLM-4.7-GGUF:Q2_K

LM Studio
Jan

vLLM

How to use ubergarm/GLM-4.7-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ubergarm/GLM-4.7-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": "ubergarm/GLM-4.7-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ubergarm/GLM-4.7-GGUF:Q2_K

Ollama
How to use ubergarm/GLM-4.7-GGUF with Ollama:
```
ollama run hf.co/ubergarm/GLM-4.7-GGUF:Q2_K
```

Unsloth Studio

How to use ubergarm/GLM-4.7-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 ubergarm/GLM-4.7-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 ubergarm/GLM-4.7-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

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

How to use ubergarm/GLM-4.7-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf ubergarm/GLM-4.7-GGUF:Q2_K

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": "ubergarm/GLM-4.7-GGUF:Q2_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

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

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf ubergarm/GLM-4.7-GGUF:Q2_K

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 ubergarm/GLM-4.7-GGUF:Q2_K

Run Hermes

hermes

Docker Model Runner
How to use ubergarm/GLM-4.7-GGUF with Docker Model Runner:
```
docker model run hf.co/ubergarm/GLM-4.7-GGUF:Q2_K
```

Lemonade

How to use ubergarm/GLM-4.7-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ubergarm/GLM-4.7-GGUF:Q2_K

Run and chat with the model

lemonade run user.GLM-4.7-GGUF-Q2_K

List all available models

lemonade list

IQ3_KS is awesome!

by mtcl - opened Dec 25, 2025

Discussion

mtcl

Dec 25, 2025

My Config:

I can load full IQ3_KS on 2x6000 Pros however i can also do it on single 6000 pro for testing and keeping another fast model on vllm on secondary GPU. here re the results that most of hybrid cpu gpu people will be interested in.

System Specifications for Hybrid CPU/GPU Inference

System Overview

OS: Ubuntu 24.04.3 LTS
Kernel: Linux 6.14.0-37-generic

Motherboard Specifications

Model: ASUS Pro WS W790E-SAGE SE

Platform: Intel W790 Chipset

CPU Specifications

Processor: Intel Xeon w9-3495X
- Cores: 56 physical cores / 112 threads
- AMX (Advanced Matrix Extensions): Full support
- NUMA Nodes: 1

GPU Specifications

Graphics Cards: 2x NVIDIA RTX PRO 6000 Black Edition

VRAM per GPU: 96 GB
Driver Version: 580.95.05
CUDA Version: 13.0
Power Consumption: 600W max each

Memory Configuration

Total RAM: 512 GB
Memory Type: DDR5 (8x DIMM slots populated)
Memory Speed: DDR5-4800+ (OC capable)
OC Supported: 6000 OC Stable
Swap: 0 GB (disabled)
Memory Architecture: 8-channel DDR5 configuration
Available for Inference: 503 GB total capacity for model loading

(base) mukul@jarvis:~/dev-ai/ik_llama.cpp$ CUDA_DEVICE_ORDER=PCI_BUS_ID CUDA_VISIBLE_DEVICES="1" ./build/bin/llama-server \
    --model /media/mukul/data/models/ubergarm/GLM-4.7-GGUF/IQ3_KS/GLM-4.7-IQ3_KS-00001-of-00005.gguf \
    --alias ubergarm/GLM-4.7 \
    --ctx-size 131072 \
    -ger \
    --merge-qkv \
    -ngl 99 \
    --n-cpu-moe 65 \
    -ctk q8_0 -ctv q8_0 \
    -ub 8192 -b 8192 \
    --threads 56 \
    --parallel 1 \
    --host 0.0.0.0 \
    --port 10002 \
    --no-mmap \
    --jinja

main: n_kv_max = 131072, n_batch = 8192, n_ubatch = 8192, flash_attn = 1, n_gpu_layers = 99, n_threads = 56, n_threads_batch = 56

|    PP |     TG |   N_KV |   T_PP s | S_PP t/s |   T_TG s | S_TG t/s |
|-------|--------|--------|----------|----------|----------|----------|
|  8192 |   2048 |      0 |   13.942 |   587.57 |  100.606 |    20.36 |
|  8192 |   2048 |   8192 |   15.024 |   545.27 |  109.013 |    18.79 |
|  8192 |   2048 |  16384 |   16.048 |   510.48 |  116.656 |    17.56 |
|  8192 |   2048 |  24576 |   17.579 |   466.01 |  126.207 |    16.23 |
|  8192 |   2048 |  32768 |   19.308 |   424.29 |  140.504 |    14.58 |
|  8192 |   2048 |  40960 |   21.887 |   374.29 |  156.982 |    13.05 |
|  8192 |   2048 |  49152 |   24.145 |   339.28 |  173.008 |    11.84 |
|  8192 |   2048 |  57344 |   26.427 |   309.98 |  193.310 |    10.59 |
|  8192 |   2048 |  65536 |   28.705 |   285.39 |  219.126 |     9.35 |
|  8192 |   2048 |  73728 |   30.605 |   267.67 |  242.317 |     8.45 |
|  8192 |   2048 |  81920 |   32.814 |   249.65 |  263.043 |     7.79 |
|  8192 |   2048 |  90112 |   34.653 |   236.40 |  282.582 |     7.25 |
|  8192 |   2048 |  98304 |   37.128 |   220.64 |  303.721 |     6.74 |
|  8192 |   2048 | 106496 |   39.245 |   208.74 |  322.416 |     6.35 |
|  8192 |   2048 | 114688 |   41.300 |   198.35 |  342.002 |     5.99 |
|  8192 |   2048 | 122880 |   43.872 |   186.73 |  360.998 |     5.67 |

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