Text Generation
Transformers
Safetensors
MLX
English
glm4_moe
glm
MOE
pruning
compression
mlx-my-repo
conversational
3-bit
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"
| [gMASK]<sop> | |
| {%- if tools -%} | |
| <|system|> | |
| # Tools | |
| You may call one or more functions to assist with the user query. | |
| You are provided with function signatures within <tools></tools> XML tags: | |
| <tools> | |
| {% for tool in tools %} | |
| {{ tool | tojson(ensure_ascii=False) }} | |
| {% endfor %} | |
| </tools> | |
| For each function call, output the function name and arguments within the following XML format: | |
| <tool_call>{function-name} | |
| <arg_key>{arg-key-1}</arg_key> | |
| <arg_value>{arg-value-1}</arg_value> | |
| <arg_key>{arg-key-2}</arg_key> | |
| <arg_value>{arg-value-2}</arg_value> | |
| ... | |
| </tool_call>{%- endif -%} | |
| {%- macro visible_text(content) -%} | |
| {%- if content is string -%} | |
| {{- content }} | |
| {%- elif content is iterable and content is not mapping -%} | |
| {%- for item in content -%} | |
| {%- if item is mapping and item.type == 'text' -%} | |
| {{- item.text }} | |
| {%- elif item is string -%} | |
| {{- item }} | |
| {%- endif -%} | |
| {%- endfor -%} | |
| {%- else -%} | |
| {{- content }} | |
| {%- endif -%} | |
| {%- endmacro -%} | |
| {%- set ns = namespace(last_user_index=-1) %} | |
| {%- for m in messages %} | |
| {%- if m.role == 'user' %} | |
| {% set ns.last_user_index = loop.index0 -%} | |
| {%- endif %} | |
| {%- endfor %} | |
| {% for m in messages %} | |
| {%- if m.role == 'user' -%}<|user|> | |
| {{ visible_text(m.content) }} | |
| {{- '/nothink' if (enable_thinking is defined and not enable_thinking and not visible_text(m.content).endswith("/nothink")) else '' -}} | |
| {%- elif m.role == 'assistant' -%} | |
| <|assistant|> | |
| {%- set reasoning_content = '' %} | |
| {%- set content = visible_text(m.content) %} | |
| {%- if m.reasoning_content is string %} | |
| {%- set reasoning_content = m.reasoning_content %} | |
| {%- else %} | |
| {%- if '</think>' in content %} | |
| {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %} | |
| {%- set content = content.split('</think>')[-1].lstrip('\n') %} | |
| {%- endif %} | |
| {%- endif %} | |
| {%- if loop.index0 > ns.last_user_index and reasoning_content -%} | |
| {{ '\n<think>' + reasoning_content.strip() + '</think>'}} | |
| {%- else -%} | |
| {{ '\n<think></think>' }} | |
| {%- endif -%} | |
| {%- if content.strip() -%} | |
| {{ '\n' + content.strip() }} | |
| {%- endif -%} | |
| {% if m.tool_calls %} | |
| {% for tc in m.tool_calls %} | |
| {%- if tc.function %} | |
| {%- set tc = tc.function %} | |
| {%- endif %} | |
| {{ '\n<tool_call>' + tc.name }} | |
| {% set _args = tc.arguments %} | |
| {% for k, v in _args.items() %} | |
| <arg_key>{{ k }}</arg_key> | |
| <arg_value>{{ v | tojson(ensure_ascii=False) if v is not string else v }}</arg_value> | |
| {% endfor %} | |
| </tool_call>{% endfor %} | |
| {% endif %} | |
| {%- elif m.role == 'tool' -%} | |
| {%- if m.content is string -%} | |
| {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %} | |
| {{- '<|observation|>' }} | |
| {%- endif %} | |
| {{- '\n<tool_response>\n' }} | |
| {{- m.content }} | |
| {{- '\n</tool_response>' }} | |
| {%- else -%} | |
| <|observation|>{% for tr in m.content %} | |
| <tool_response> | |
| {{ tr.output if tr.output is defined else tr }} | |
| </tool_response>{% endfor -%} | |
| {% endif -%} | |
| {%- elif m.role == 'system' -%} | |
| <|system|> | |
| {{ visible_text(m.content) }} | |
| {%- endif -%} | |
| {%- endfor -%} | |
| {%- if add_generation_prompt -%} | |
| <|assistant|>{{- '\n<think></think>' if (enable_thinking is defined and not enable_thinking) else '' -}} | |
| {%- endif -%} |