Instructions to use QuantPasture/GLM-5.1-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 QuantPasture/GLM-5.1-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 QuantPasture/GLM-5.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantPasture/GLM-5.1-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 QuantPasture/GLM-5.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantPasture/GLM-5.1-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 QuantPasture/GLM-5.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantPasture/GLM-5.1-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 QuantPasture/GLM-5.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantPasture/GLM-5.1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantPasture/GLM-5.1-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use QuantPasture/GLM-5.1-GGUF with Ollama:
```
ollama run hf.co/QuantPasture/GLM-5.1-GGUF:Q4_K_M
```
Unsloth Desktop

How to use QuantPasture/GLM-5.1-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf QuantPasture/GLM-5.1-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": "QuantPasture/GLM-5.1-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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

Lemonade

How to use QuantPasture/GLM-5.1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantPasture/GLM-5.1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.GLM-5.1-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use QuantPasture/GLM-5.1-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 QuantPasture/GLM-5.1-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 QuantPasture/GLM-5.1-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use QuantPasture/GLM-5.1-GGUF with OpenClaw:

Start the llama.cpp server

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

GLM-5.1-GGUF

File size: 1,427 Bytes

---
base_model:
- zai-org/GLM-5.1
---
## Notes
- 05/02/26: IQ3_S quant coming a bit later, quantization crashed so need to redo it.

## Model
This repo contains specialized MoE-quants for zai-org/GLM-5.1. The idea being that given the huge size of the FFN tensors compared to the rest of the tensors in the model, it should be possible to achieve a better quality while keeping the overall size of the entire model smaller compared to a similar naive quantization. To that end, the quantization type default is kept in high quality and the FFN UP + FFN GATE tensors are quanted down along with the FFN DOWN tensors.

| Quant | Size | Mixture | PPL | 1-(Mean PPL(Q)/PPL(base)) | KLD |
| :--------- | :--------- | :------- | :------- | :------- | :------- |
| Q5_K_M | 520.08 GiB (5.93 BPW) | Q8_0 / Q5_K / Q5_K / Q6_K | 2.732420 ± 0.015015 | +0.3411% | 0.020247 ± 0.000173 |
| Q4_K_M | 432.80 GiB (4.93 BPW) | Q8_0 / Q4_K / Q4_K / Q5_K | 2.754593 ± 0.015142 | +1.1553% | 0.037406 ± 0.000308 |
| IQ4_XS | 336.61 GiB (3.84 BPW) | Q8_0 / IQ3_S / IQ3_S / IQ4_XS | 2.892748 ± 0.015981 | +6.2287% | 0.099818 ± 0.000754 |
| IQ3_S | 259.89 GiB (2.96 BPW) | Q6_K / IQ2_S / IQ2_S / IQ3_S | 3.282336 ± 0.018782 | +20.5353% | 0.262398 ± 0.001686 |

![kld_graph](kld_data/01_kld_vs_filesize.png "Chart showing Pareto KLD analysis of quants")
![ppl_graph](kld_data/02_ppl_vs_filesize.png "Chart showing Pareto PPL analysis of quants")