Instructions to use AesSedai/GLM-5.3-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 AesSedai/GLM-5.3-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 AesSedai/GLM-5.3-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AesSedai/GLM-5.3-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 AesSedai/GLM-5.3-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AesSedai/GLM-5.3-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 AesSedai/GLM-5.3-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf AesSedai/GLM-5.3-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 AesSedai/GLM-5.3-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AesSedai/GLM-5.3-GGUF:Q4_K_M

Use Docker

docker model run hf.co/AesSedai/GLM-5.3-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use AesSedai/GLM-5.3-GGUF with Ollama:
```
ollama run hf.co/AesSedai/GLM-5.3-GGUF:Q4_K_M
```
Unsloth Desktop

How to use AesSedai/GLM-5.3-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AesSedai/GLM-5.3-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": "AesSedai/GLM-5.3-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use AesSedai/GLM-5.3-GGUF with Docker Model Runner:
```
docker model run hf.co/AesSedai/GLM-5.3-GGUF:Q4_K_M
```

Lemonade

How to use AesSedai/GLM-5.3-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AesSedai/GLM-5.3-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.GLM-5.3-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use AesSedai/GLM-5.3-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 AesSedai/GLM-5.3-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 AesSedai/GLM-5.3-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use AesSedai/GLM-5.3-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AesSedai/GLM-5.3-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 "AesSedai/GLM-5.3-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"

This repo contains specialized MoE-quants for zai-org/GLM-5.3-BF16. 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
Q8_0	745.77 GiB (8.50 BPW)	Q8_0	2.674779 ± 0.013836	+0.0174%	0.013080 ± 0.000123
Q5_K_M	523.07 GiB (5.96 BPW)	Q8_0 / Q5_K / Q5_K / Q6_K	2.680808 ± 0.013885	+0.2428%	0.020301 ± 0.000174
Q4_K_M	436.94 GiB (4.98 BPW)	Q8_0 / Q4_K / Q4_K / Q5_K	2.699454 ± 0.013914	+0.9400%	0.037094 ± 0.000294
IQ4_XS	342.01 GiB (3.90 BPW)	Q8_0 / IQ3_S / IQ3_S / IQ4_XS	2.841042 ± 0.014851	+6.2344%	0.101583 ± 0.000741
IQ3_S	265.93 GiB (3.03 BPW)	Q6_K / IQ2_S / IQ2_S / IQ3_S	3.257199 ± 0.017856	+21.7956%	0.275247 ± 0.001717
IQ2_S	241.32 GiB (2.75 BPW)	Q6_K / IQ2_XS / IQ2_XS / IQ3_XXS	3.512105 ± 0.019532	+31.3273%	0.374183 ± 0.002137