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

Unreleased

by jpsequeira - opened Mar 25

Discussion

jpsequeira

Mar 25

Hey,

Could you explain why you have in your perplexity chart quants with the unreleased label?
IQ4_NL seems to be the best you have there but it's unreleased, any way to get a hold of that quant?

Thanks

ubergarm

Owner Mar 27

Could you explain why you have in your perplexity chart quants with the unreleased label?

I made some test quants and used them for benchmarking relative performance. Even if not released, it gives some relative quality comparisons.

IQ4_NL seems to be the best you have there but it's unreleased, any way to get a hold of that quant?

I didn't upload the larger models to save space on my public repo quota. and in general I focus on releasing ik_llama.cpp exclusive quants.

Check out: https://huggingface.co/AesSedai/GLM-5-GGUF as AesSedai uses similar style recipes as me focusing on mainline MoEs.

jukofyork

Apr 7

•

edited Apr 7

Can you remember if you used the imatrix for your IQ4_NL version?

Just testing a custom version of Q4_K that should work better with the QAT they used and got:

Final estimate: PPL over 565 chunks for n_ctx=512 = 2.6677 +/- 0.01420

(no imatrix used)

ubergarm

Owner Apr 7

@jukofyork

GLM-5.1 just landed!!

Looking at my logs, yes I used the imatrix in this repo with my IQ4_NL.

Here is my perplexity logs for that run:

$ grep -E '(Final|model size)' perplexity-GLM-5-smol-IQ4_NL.log
llm_load_print_meta: model size       = 405.502 GiB (4.621 BPW)
Final estimate: PPL over 565 chunks for n_ctx=512 = 2.6730 +/- 0.01422

Here is the exact recipe I used for that one:

👈 Details

#!/usr/bin/env bash

custom="
# 79 Repeating Layers [0-78]

## Attention [0-78]
blk\..*\.attn_k_b\.weight=q8_0
blk\..*\.attn_v_b\.weight=q8_0
blk\..*\.attn_kv_a_mqa\.weight=q8_0
blk\..*\.attn_q_a\.weight=q8_0
blk\..*\.attn_q_b\.weight=q8_0
blk\..*\.attn_output\.weight=q8_0

# First 3 Dense Layers [0-2]
blk\..*\.ffn_down\.weight=iq6_k
blk\..*\.ffn_(gate|up)\.weight=iq6_k

# Shared Expert Layers [3-78]
blk\..*\.ffn_down_shexp\.weight=iq6_k
blk\..*\.ffn_(gate|up)_shexp\.weight=iq6_k

# Routed Experts Layers [3-78]
# NOTE: blk.78.* NOT implemented at time of quantizing so no imatrix data available
blk\.(78)\.ffn_down_exps\.weight=iq6_k
blk\.(78)\.ffn_(gate|up)_exps\.weight=iq6_k
blk\..*\.ffn_down_exps\.weight=iq4_nl
blk\..*\.ffn_(gate|up)_exps\.weight=iq4_nl

# Lightning indexer tensors [0-78]
# NOTE: indexer.* NOT implemented at time of quantizing so no imatrix data available
blk\..*\.indexer\.proj\.weight=q8_0
blk\..*\.indexer\.attn_k\.weight=q8_0
blk\..*\.indexer\.attn_q_b\.weight=q8_0

# NextN MTP Layer [78]
# NOTE: nextn.* NOT implemented at time of quantizing so no imatrix data available
blk\..*\.nextn\.eh_proj\.weight=q8_0

# Non-Repeating Layers
token_embd\.weight=iq6_k
output\.weight=iq6_k
"

custom=$(
  echo "$custom" | grep -v '^#' | \
  sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
)

numactl -N ${SOCKET} -m ${SOCKET} \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/data/models/ubergarm/GLM-5-GGUF/imatrix-GLM-5-BF16.dat \
    /mnt/data/models/ubergarm/GLM-5-GGUF/GLM-256x22B-5-BF16-00001-of-00033.gguf \
    /mnt/data/models/ubergarm/GLM-5-GGUF/GLM-5-smol-IQ4_NL.gguf \
    IQ4_NL \
    128

jukofyork

Apr 7

Thanks, so maybe my custom Q4_K code targeted at their QAT is working then.

I kinda expected it to be closer to BF16 after Kimi-K2-Thinking, but realised we never actually got the "real" BF16 of that model and could only compare back with the INT4 model they gave us!

I'm away from home and will take me several days to download GLM-5.1, but I posted the custom Q4_K code here:

https://github.com/ggml-org/llama.cpp/pull/19460#issuecomment-4200617220

jpsequeira

Apr 7

GLM-5.1 just landed!!

Awaiting your magic 😀

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