Instructions to use 0xSero/GLM-5.1-444B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use 0xSero/GLM-5.1-444B-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 0xSero/GLM-5.1-444B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf 0xSero/GLM-5.1-444B-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 0xSero/GLM-5.1-444B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf 0xSero/GLM-5.1-444B-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 0xSero/GLM-5.1-444B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf 0xSero/GLM-5.1-444B-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 0xSero/GLM-5.1-444B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf 0xSero/GLM-5.1-444B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/0xSero/GLM-5.1-444B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use 0xSero/GLM-5.1-444B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "0xSero/GLM-5.1-444B-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": "0xSero/GLM-5.1-444B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/0xSero/GLM-5.1-444B-GGUF:Q4_K_M

Ollama
How to use 0xSero/GLM-5.1-444B-GGUF with Ollama:
```
ollama run hf.co/0xSero/GLM-5.1-444B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use 0xSero/GLM-5.1-444B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf 0xSero/GLM-5.1-444B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "0xSero/GLM-5.1-444B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use 0xSero/GLM-5.1-444B-GGUF with Docker Model Runner:
```
docker model run hf.co/0xSero/GLM-5.1-444B-GGUF:Q4_K_M
```

Lemonade

How to use 0xSero/GLM-5.1-444B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull 0xSero/GLM-5.1-444B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.GLM-5.1-444B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use 0xSero/GLM-5.1-444B-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 0xSero/GLM-5.1-444B-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 0xSero/GLM-5.1-444B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use 0xSero/GLM-5.1-444B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf 0xSero/GLM-5.1-444B-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 "0xSero/GLM-5.1-444B-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"

Support this work → · X · GitHub · REAP paper · Cerebras REAP

GLM-5.1-444B-GGUF

GGUF quantization of zai-org/GLM-5.1.

At a glance


Base model	zai-org/GLM-5.1
Format	GGUF
Total params	444B
Active / token	14B
Experts / layer	—
Layers	—
Hidden size	—
Context	—
On-disk size	283 GB

Which variant should I pick?

Variant	Format	Link
`GLM-5.1-444B`	BF16	link
`GLM-5.1-444B-GGUF` (this)	GGUF	link
`GLM-5.1-478B-NVFP4`	NVFP4	link
`GLM-5.1-555B`	BF16	link
`GLM-5.1-555B-GGUF`	GGUF	link
`GLM-5.1-555B-NVFP4`	NVFP4	link
`GLM-5.1-555B-W4A16`	W4A16	link

This model has repetition degeneration. Use the 25% pruned version instead.

Use this instead: 0xSero/GLM-5.1-555B-GGUF

What is wrong with this model?

This is a Q4_K_M GGUF of the 40% expert-pruned GLM-5.1 (154/256 experts retained). It suffers from repetition degeneration - the model enters infinite loops when generating code, structured output, or any long-form content requiring syntactic templates.

Measured degeneration rates:

29% overall (13/45 probes degenerate in fuzz testing)
40% of code generation tasks loop (red-black trees, chess engines, regex, B-trees)
75% of structured output tasks loop (comparison tables, API specs, enum lists)
18% of Terminal-Bench probes loop (9/50)
30% of SWE-bench Pro probes loop (12/40)

Root cause:

Removing 40% of experts (102 per layer) exceeds the model's tolerance for expert pruning. The remaining 154 experts cannot cover the full routing distribution needed for coherent long-form generation. The degeneration compounds over sequence length - short outputs (<512 tokens) work fine, but anything over ~600-1000 words risks entering a repetition loop.

The fix:

The 25% pruned variant (192/256 experts, 555B) completely eliminates repetition loops while maintaining competitive quality:

0/220 benchmark probes had repetition loops
Terminal-Bench: 88% proxy pass rate
SWE-Pro: 66% proxy pass rate

Use 0xSero/GLM-5.1-555B-GGUF instead.

License & citation

License inherited from the base model.

@misc{lasby2025reap,
  title  = {REAP the Experts: Why Pruning Prevails for One-Shot MoE Compression},
  author = {Mike Lasby and Ivan Lazarevich and Nish Sinnadurai and Sean Lie and Yani Ioannou and Vithursan Thangarasa},
  year   = {2025}, eprint = {2510.13999}, archivePrefix = {arXiv}
}

Model tree for 0xSero/GLM-5.1-444B-GGUF

Base model

zai-org/GLM-5.1

Quantized

(35)

this model

Collection including 0xSero/GLM-5.1-444B-GGUF

GLM — REAP

Collection

REAP-pruned & quantized GLM-4.x / 5 / 5.1 (+ Flash fine-tunes). • 34 items • Updated about 15 hours ago • 2

Paper for 0xSero/GLM-5.1-444B-GGUF

REAP the Experts: Why Pruning Prevails for One-Shot MoE compression

Paper • 2510.13999 • Published Oct 15, 2025 • 23

0xSero
/

GLM-5.1-444B-GGUF

GLM-5.1-444B-GGUF

At a glance

Which variant should I pick?

This model has repetition degeneration. Use the 25% pruned version instead.

What is wrong with this model?

Measured degeneration rates:

Root cause:

The fix:

License & citation

Sponsors

Model tree for 0xSero/GLM-5.1-444B-GGUF

Collection including 0xSero/GLM-5.1-444B-GGUF

GLM — REAP

Paper for 0xSero/GLM-5.1-444B-GGUF

REAP the Experts: Why Pruning Prevails for One-Shot MoE compression