Instructions to use mlx-community/GLM-5.3-mixed-4_5bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlx-community/GLM-5.3-mixed-4_5bit with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("mlx-community/GLM-5.3-mixed-4_5bit")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Transformers

How to use mlx-community/GLM-5.3-mixed-4_5bit with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="mlx-community/GLM-5.3-mixed-4_5bit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mlx-community/GLM-5.3-mixed-4_5bit")
model = AutoModelForCausalLM.from_pretrained("mlx-community/GLM-5.3-mixed-4_5bit", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

vLLM

How to use mlx-community/GLM-5.3-mixed-4_5bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mlx-community/GLM-5.3-mixed-4_5bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mlx-community/GLM-5.3-mixed-4_5bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mlx-community/GLM-5.3-mixed-4_5bit

SGLang

How to use mlx-community/GLM-5.3-mixed-4_5bit with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "mlx-community/GLM-5.3-mixed-4_5bit" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mlx-community/GLM-5.3-mixed-4_5bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "mlx-community/GLM-5.3-mixed-4_5bit" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mlx-community/GLM-5.3-mixed-4_5bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

How to use mlx-community/GLM-5.3-mixed-4_5bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "mlx-community/GLM-5.3-mixed-4_5bit"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "mlx-community/GLM-5.3-mixed-4_5bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use mlx-community/GLM-5.3-mixed-4_5bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "mlx-community/GLM-5.3-mixed-4_5bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "mlx-community/GLM-5.3-mixed-4_5bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "mlx-community/GLM-5.3-mixed-4_5bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use mlx-community/GLM-5.3-mixed-4_5bit with Docker Model Runner:
```
docker model run hf.co/mlx-community/GLM-5.3-mixed-4_5bit
```

Hermes Agent

How to use mlx-community/GLM-5.3-mixed-4_5bit with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "mlx-community/GLM-5.3-mixed-4_5bit"

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 mlx-community/GLM-5.3-mixed-4_5bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use mlx-community/GLM-5.3-mixed-4_5bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "mlx-community/GLM-5.3-mixed-4_5bit"

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 "mlx-community/GLM-5.3-mixed-4_5bit" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

mlx-community/GLM-5.3-mixed-4_5bit

This model mlx-community/GLM-5.3-mixed-4_5bit was converted to MLX format from zai-org/GLM-5.3-BF16 using mlx-lm version 0.31.3 (with PR #1410).

Note that this quant is using the GLM-5.3-BF16 as base.

Testing various quant recipes, these often start to overthink and redoing "decisions". Sadly, perplexity and KLD calculations don't always tell the full story. Getting the mix of the recipe right involves:

Ensuring that the model does not overthink and redo work, leading to double the token usage
Keeping more knowledge in the experts
Protect sensitive layers
Getting the quant to clearly understand the instructions
Not slowing down the processing
Keeping space for enough context at max reasoning

This is created for people using a single Apple Mac Studio M3 Ultra with 512 GB. The 4-bit version of GLM-5.3 fits comfortably.

You can find more similar MLX model quants for Apple Mac Studio with 512 GB at https://huggingface.co/bibproj

pip install mlx-lm

mlx_lm.generate --model mlx-community/GLM-5.3-mixed-4_5bit --prompt "Hi"

Enjoy!

Downloads last month: -

Safetensors

Model size

124B params

Tensor type

BF16

U32

F32

MLX

Hardware compatibility

4-bit

Model tree for mlx-community/GLM-5.3-mixed-4_5bit

Base model

zai-org/GLM-5.3-BF16

Quantized

(15)

this model