Instructions to use ubergarm/GLM-5-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ubergarm/GLM-5-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ubergarm/GLM-5-GGUF",
	filename="IQ2_KL/GLM-5-IQ2_KL-00001-of-00007.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-5-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 ubergarm/GLM-5-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf ubergarm/GLM-5-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ubergarm/GLM-5-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf ubergarm/GLM-5-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-5-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf ubergarm/GLM-5-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-5-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ubergarm/GLM-5-GGUF:Q2_K

Use Docker

docker model run hf.co/ubergarm/GLM-5-GGUF:Q2_K

LM Studio
Jan

vLLM

How to use ubergarm/GLM-5-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ubergarm/GLM-5-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-5-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ubergarm/GLM-5-GGUF:Q2_K

Ollama
How to use ubergarm/GLM-5-GGUF with Ollama:
```
ollama run hf.co/ubergarm/GLM-5-GGUF:Q2_K
```

Unsloth Studio

How to use ubergarm/GLM-5-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-5-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-5-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-5-GGUF to start chatting

How to use ubergarm/GLM-5-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ubergarm/GLM-5-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-5-GGUF:Q2_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ubergarm/GLM-5-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 ubergarm/GLM-5-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-5-GGUF:Q2_K

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use ubergarm/GLM-5-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ubergarm/GLM-5-GGUF:Q2_K

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 "ubergarm/GLM-5-GGUF:Q2_K" \
  --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 ubergarm/GLM-5-GGUF with Docker Model Runner:
```
docker model run hf.co/ubergarm/GLM-5-GGUF:Q2_K
```

Lemonade

How to use ubergarm/GLM-5-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ubergarm/GLM-5-GGUF:Q2_K

Run and chat with the model

lemonade run user.GLM-5-GGUF-Q2_K

List all available models

lemonade list

96GB VRAM and 192GB of RAM desktop system.

by coughmedicine - opened Feb 15

Discussion

coughmedicine

Feb 15

My system is right in the middle of having plenty of room after smol-IQ2_KS but not enough for IQ3_KS. Is there any benefit to a quant in that middle ground?

ubergarm

Owner Feb 15

@coughmedicine

Heya, i forget is your 96GB in a single GPU or multiple GPUs? (-sm graph works for 2 or more GPUs, but not on this model yet afaik).

So you have total 288GiB RAM+VRAM let's see...

There might be a good spot there, let's check what is available from https://huggingface.co/AesSedai/GLM-5-GGUF no, he needs to get more public repo storage to keep uploadnig more quants i believe at the moment. but i like his recipes for mainline.

Here is where I am currently in my testing:

I might release 2 more quants the, something a bit bigger, and something fitting that gap you mention.

Perhaps an IQ2_KL would do the trick. Keep an eye out and see if I get something out by tomorrow!

Thanks!

ubergarm

Owner Feb 15

Okay, cooked up an interesting mix of iq2_kl for ffn_(gate|up)_exps and iq3_ks for ffn_down_exps giving us 255.84 GiB model size roughly 3bpw overall. Given GLM-5 uses MLA the kv-cache is very efficient for context so you it might be just right assuming you're not running much else on there.

I'm testing perplexity now to see how it holds up!

ubergarm

Owner Feb 15

@coughmedicine

The IQ2_KL looks good enough perplexity for me to release, keep us posted how it works out for you!

https://huggingface.co/ubergarm/GLM-5-GGUF/resolve/main/images/perplexity.png

coughmedicine

Feb 15

Thanks! I will give it a try!

coughmedicine

Feb 15

Tight is right! 93088MiB VRAM and 179336MiB RAM. I'm still experimenting with launch parameters, but after an hour of testing it is different from the other quants in a way I can't describe but I really hope the M5Ultra is everything the benchmarks predict because I think I will really like this model for chatting but I read faster then my current setup can output.
Oh, I get ~7tg/s, pretty respectable.

ubergarm

Owner Feb 15

Sweet, glad it fits! I'm gonna try running that same IQ_KL it on level1tech Wendell's GPU rig:

AMD 7965WX 24x Core 8x32GiB DDR5@4800 + Dual RTX A6000 (96GB Total VRAM) Driver: 580.105.08 CUDA: 13.0

If I get it going, I'll try to make a llama-sweep-bench graph to see how PP/TG fall off with longer context

ubergarm

Owner Feb 16

•

edited Feb 16

@coughmedicine

oof, i knew this GLM-5 had more active weights than many other MoEs of its size class, but yeesh...

maybe there is a better way I could be running it? I had some problems starting up with --n-cpu-moe XX for some reason on this specific rig. I've mostly run it CPU only hah... I didn't try quantizing kv-cache at is is pretty efficient 128k context at 11232.00 MiB. It might run better on a single 96GB GPU like 6000 PRO probably, but not how much without more DRAM memory bandwidth.

Anyway, here is what I tried:

# example api server
model=/mnt/raid/models/ubergarm/GLM-5-GGUF/GLM-5-IQ2_KL-00001-of-00007.gguf
./build/bin/llama-server \
    --model "$model" \
    --alias ubergarm/GLM-5 \
    --ctx-size 131072 \
    -ctk f16 \
    -ger \
    --merge-qkv \
    -mla 3 -amb 1024 \
    -ot "blk\.(3|4|5|6|7|8|9|10|11|11)\.ffn_(gate|up|down)_exps.*=CUDA0" \
    -ot "blk\.(64|65|66|67|68|69|70|71|72|73|74|75|76|77)\.ffn_(gate|up|down)_exps.*=CUDA1" \
    --cpu-moe \
    -ub 4096 -b 4096 \
    --threads 24 \
    --host 127.0.0.1 \
    --port 8080 \
    --no-mmap \
    --jinja

# llama-sweep-bench used for image below
./build/bin/llama-sweep-bench \
    --model "$model" \
    --ctx-size 131072 \
    -ctk f16 \
    -ger \
    --merge-qkv \
    -mla 3 -amb 1024 \
    -ot "blk\.(3|4|5|6|7|8|9|10|11|11)\.ffn_(gate|up|down)_exps.*=CUDA0" \
    -ot "blk\.(64|65|66|67|68|69|70|71|72|73|74|75|76|77)\.ffn_(gate|up|down)_exps.*=CUDA1" \
    --cpu-moe \
    -ub 4096 -b 4096 \
    --threads 24 \
    --no-mmap \
    --warmup-batch \
    --n-predict 64

# CPU sweep bench
model=/mnt/raid/hf/GLM-5-GGUF/IQ2_KL/GLM-5-IQ2_KL-00001-of-00007.gguf

numactl -N ${SOCKET} -m ${SOCKET} \
./build/bin/llama-sweep-bench \
    --model "$model"\
    --ctx-size 131072 \
    -ctk q8_0 \
    -ger \
    --merge-qkv \
    -mla 3 \
    --threads 92 \
    --threads-batch 128 \
    -ub 4096 -b 4096 \
    --no-mmap \
    --numa numactl \
    --warmup-batch \
    --n-predict 64

ubergarm

Owner Feb 19

I might be running it non-optimally, it might actually go faster with less sparse experts offloaded onto GPUs, need to do some research when I have a spare sec: https://github.com/ikawrakow/ik_llama.cpp/pull/1288#issuecomment-3929312142

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