Instructions to use Wwayu/GLM-4.6-REAP-218B-A32B-mlx-3Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Wwayu/GLM-4.6-REAP-218B-A32B-mlx-3Bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Wwayu/GLM-4.6-REAP-218B-A32B-mlx-3Bit") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Wwayu/GLM-4.6-REAP-218B-A32B-mlx-3Bit") model = AutoModelForCausalLM.from_pretrained("Wwayu/GLM-4.6-REAP-218B-A32B-mlx-3Bit", 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]:])) - MLX
How to use Wwayu/GLM-4.6-REAP-218B-A32B-mlx-3Bit 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("Wwayu/GLM-4.6-REAP-218B-A32B-mlx-3Bit") 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) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- vLLM
How to use Wwayu/GLM-4.6-REAP-218B-A32B-mlx-3Bit with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Wwayu/GLM-4.6-REAP-218B-A32B-mlx-3Bit" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Wwayu/GLM-4.6-REAP-218B-A32B-mlx-3Bit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Wwayu/GLM-4.6-REAP-218B-A32B-mlx-3Bit
- SGLang
How to use Wwayu/GLM-4.6-REAP-218B-A32B-mlx-3Bit 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 "Wwayu/GLM-4.6-REAP-218B-A32B-mlx-3Bit" \ --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": "Wwayu/GLM-4.6-REAP-218B-A32B-mlx-3Bit", "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 "Wwayu/GLM-4.6-REAP-218B-A32B-mlx-3Bit" \ --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": "Wwayu/GLM-4.6-REAP-218B-A32B-mlx-3Bit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Pi
How to use Wwayu/GLM-4.6-REAP-218B-A32B-mlx-3Bit with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "Wwayu/GLM-4.6-REAP-218B-A32B-mlx-3Bit"
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": "Wwayu/GLM-4.6-REAP-218B-A32B-mlx-3Bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use Wwayu/GLM-4.6-REAP-218B-A32B-mlx-3Bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "Wwayu/GLM-4.6-REAP-218B-A32B-mlx-3Bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "Wwayu/GLM-4.6-REAP-218B-A32B-mlx-3Bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Wwayu/GLM-4.6-REAP-218B-A32B-mlx-3Bit", "messages": [ {"role": "user", "content": "Hello"} ] }' - Docker Model Runner
How to use Wwayu/GLM-4.6-REAP-218B-A32B-mlx-3Bit with Docker Model Runner:
docker model run hf.co/Wwayu/GLM-4.6-REAP-218B-A32B-mlx-3Bit
- Hermes Agent
How to use Wwayu/GLM-4.6-REAP-218B-A32B-mlx-3Bit 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 "Wwayu/GLM-4.6-REAP-218B-A32B-mlx-3Bit"
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 Wwayu/GLM-4.6-REAP-218B-A32B-mlx-3Bit
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Wwayu/GLM-4.6-REAP-218B-A32B-mlx-3Bit with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "Wwayu/GLM-4.6-REAP-218B-A32B-mlx-3Bit"
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 "Wwayu/GLM-4.6-REAP-218B-A32B-mlx-3Bit" \ --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"
Thanks & Feedback
First of all, thank you for taking all the time and effort involved to download, convert/quantize and upload this. I've been excited about the possibility of running GLM 4.6 with the help of pruning nd was waiting for someone to upload a quant that could run on my 96GB Mac Studio.
I had previously tried a 178B quant by someone else, Q3K GGUF format, but unfortunately it seemed to suffer from a catastrophic loss of general knowledge, not knowing who various people I prompted it about were.
I downloaded your own 3-bit MLX quant of the 218B version hoping a less pruned version might retain more general knowledge, but unfortunately it seems to have the same problem, perhaps because the dataset is optimised for tasks like coding and the experts pruned impact on general knowledge.
The responses themselves are coherent and the model seems to understand what's being asked, with just a bit of confusion, so the quant itself is fine, but the model does not seem to know anything about the people - fictional or real - I'm asking about, making it unsuitable for me to use for RP/creative writing which is my intended use.
Did you have an opportunity to try these quants yourself, and how did you find it? Does it work for your own purposes?



Thanks for sharing your work.
The MLX is very welcome.
Model runs in latest LMstudio.
Seems better than GLM Air 4.5 for math/aerodynamic stuff.
Amazing that it works with half the experts removed.
Perhaps what's needed here is pruned models to specialise in different areas. It'd be nice to see one tailored to creative writing, general knowledge and roleplay. Pruning makes models more accessible, but they're not much use if they don't support what you wanted to use them for because those experts have been pruned.
Agree, LLMs lose a lot of world knowledge with REAP.
They can still reason about how many angels can dance on the head of a pin.
I went back to https://huggingface.co/nightmedia/unsloth-GLM-4.5-Air-qx64-mlx
Brains, world knowlege, and very compressed.