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

llama.cpp

How to use ubergarm/GLM-5-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-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-server -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 new

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

Pi new

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-server -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-server -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

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

Testing IQ3_KS

by shewin - opened Feb 16

Discussion

shewin

Feb 16

W790E Sage + QYFS + 512G + RTX5090

Computed blk.78.attn_kv_b.weight as 512 x 28672 and stored in buffer CUDA0
===================================== llama_init_from_model: f16
llama_init_from_model: n_ctx = 80128
llama_init_from_model: n_batch = 4096
llama_init_from_model: n_ubatch = 4096
llama_init_from_model: flash_attn = 1
llama_init_from_model: mla_attn = 3
llama_init_from_model: attn_max_b = 512
llama_init_from_model: fused_moe = 1
llama_init_from_model: grouped er = 1
llama_init_from_model: fused_up_gate = 1
llama_init_from_model: fused_mmad = 1
llama_init_from_model: rope_cache = 0
llama_init_from_model: graph_reuse = 1
llama_init_from_model: k_cache_hadam = 0
llama_init_from_model: split_mode_graph_scheduling = 0
llama_init_from_model: reduce_type = f16
llama_init_from_model: sched_async = 0
llama_init_from_model: ser = -1, 0
llama_init_from_model: freq_base = 1000000.0
llama_init_from_model: freq_scale = 1
llama_kv_cache_init: CUDA0 KV buffer size = 3647.83 MiB
llama_init_from_model: KV self size = 3647.79 MiB, c^KV (q8_0): 3647.79 MiB, kv^T: not used
llama_init_from_model: CUDA_Host output buffer size = 0.59 MiB
llama_init_from_model: CUDA0 compute buffer size = 8385.02 MiB
llama_init_from_model: CUDA_Host compute buffer size = 722.05 MiB
llama_init_from_model: graph nodes = 5102
llama_init_from_model: graph splits = 152
XXXXXXXXXXXXXXXXXXXXX Setting only active experts offload

main: n_kv_max = 80128, n_batch = 4096, n_ubatch = 4096, flash_attn = 1, n_gpu_layers = 99, n_threads = 101, n_threads_batch = 101

PP	TG	N_KV	T_PP s	S_PP t/s	T_TG s	S_TG t/s
4096	1024	0	41.114	99.63	67.358	15.20
4096	1024	4096	33.055	123.91	68.527	14.94
4096	1024	8192	33.924	120.74	82.819	12.36
4096	1024	12288	34.525	118.64	91.165	11.23
4096	1024	16384	35.190	116.40	91.311	11.21
4096	1024	20480	35.921	114.03	72.911	14.04

shewin

Feb 16

Really playable pacman and very good model!
but it takes very long time.

ubergarm

Owner Feb 16

@shewin

Yes, my impression too. It seems very smart and capable with opencode etc, but without lightning indexer or mtp nextn tensor support it is slow given the A40B.

Keep an eye for https://huggingface.co/Qwen/Qwen3.5-397B-A17B but need ik support here: https://github.com/ikawrakow/ik_llama.cpp/issues/1255

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment