Instructions to use mrtoots/GLM-4.7-mlx-6Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mrtoots/GLM-4.7-mlx-6Bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="mrtoots/GLM-4.7-mlx-6Bit") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("mrtoots/GLM-4.7-mlx-6Bit") model = AutoModelForCausalLM.from_pretrained("mrtoots/GLM-4.7-mlx-6Bit") 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 mrtoots/GLM-4.7-mlx-6Bit 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("mrtoots/GLM-4.7-mlx-6Bit") 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 mrtoots/GLM-4.7-mlx-6Bit with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "mrtoots/GLM-4.7-mlx-6Bit" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mrtoots/GLM-4.7-mlx-6Bit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/mrtoots/GLM-4.7-mlx-6Bit
- SGLang
How to use mrtoots/GLM-4.7-mlx-6Bit 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 "mrtoots/GLM-4.7-mlx-6Bit" \ --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": "mrtoots/GLM-4.7-mlx-6Bit", "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 "mrtoots/GLM-4.7-mlx-6Bit" \ --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": "mrtoots/GLM-4.7-mlx-6Bit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Pi
How to use mrtoots/GLM-4.7-mlx-6Bit with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "mrtoots/GLM-4.7-mlx-6Bit"
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "mrtoots/GLM-4.7-mlx-6Bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use mrtoots/GLM-4.7-mlx-6Bit 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 "mrtoots/GLM-4.7-mlx-6Bit"
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 mrtoots/GLM-4.7-mlx-6Bit
Run Hermes
hermes
- MLX LM
How to use mrtoots/GLM-4.7-mlx-6Bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "mrtoots/GLM-4.7-mlx-6Bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "mrtoots/GLM-4.7-mlx-6Bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mrtoots/GLM-4.7-mlx-6Bit", "messages": [ {"role": "user", "content": "Hello"} ] }' - Docker Model Runner
How to use mrtoots/GLM-4.7-mlx-6Bit with Docker Model Runner:
docker model run hf.co/mrtoots/GLM-4.7-mlx-6Bit
Upload chat_template.jinja with huggingface_hub
Browse files- chat_template.jinja +86 -0
chat_template.jinja
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[gMASK]<sop>
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{%- if tools -%}
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<|system|>
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# Tools
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You may call one or more functions to assist with the user query.
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You are provided with function signatures within <tools></tools> XML tags:
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<tools>
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{% for tool in tools %}
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{{ tool | tojson(ensure_ascii=False) }}
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{% endfor %}
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</tools>
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For each function call, output the function name and arguments within the following XML format:
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<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 -%}
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{%- macro visible_text(content) -%}
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{%- if content is string -%}
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{{- content }}
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{%- elif content is iterable and content is not mapping -%}
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{%- for item in content -%}
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{%- if item is mapping and item.type == 'text' -%}
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{{- item.text }}
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{%- elif item is string -%}
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{{- item }}
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{%- endif -%}
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{%- endfor -%}
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{%- else -%}
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{{- content }}
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{%- endif -%}
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{%- endmacro -%}
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{%- set ns = namespace(last_user_index=-1) %}
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{%- for m in messages %}
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{%- if m.role == 'user' %}
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{% set ns.last_user_index = loop.index0 -%}
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{%- endif %}
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{%- endfor %}
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{% for m in messages %}
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{%- if m.role == 'user' -%}<|user|>{{ visible_text(m.content) }}
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{%- elif m.role == 'assistant' -%}
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<|assistant|>
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{%- set reasoning_content = '' %}
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{%- set content = visible_text(m.content) %}
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{%- if m.reasoning_content is string %}
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{%- set reasoning_content = m.reasoning_content %}
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{%- else %}
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{%- if '</think>' in content %}
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{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
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{%- set content = content.split('</think>')[-1].lstrip('\n') %}
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{%- endif %}
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{%- endif %}
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{%- if ((clear_thinking is defined and not clear_thinking) or loop.index0 > ns.last_user_index) and reasoning_content -%}
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{{ '<think>' + reasoning_content.strip() + '</think>'}}
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{%- else -%}
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{{ '</think>' }}
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{%- endif -%}
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{%- if content.strip() -%}
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{{ content.strip() }}
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{%- endif -%}
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{% if m.tool_calls %}
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{% for tc in m.tool_calls %}
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{%- if tc.function %}
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{%- set tc = tc.function %}
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{%- endif %}
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{{- '<tool_call>' + tc.name -}}
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{% 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 %}
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{% endif %}
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{%- elif m.role == 'tool' -%}
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{%- if m.content is string -%}
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{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|observation|>' }}
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{%- endif %}
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{{- '<tool_response>' }}
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{{- m.content }}
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{{- '</tool_response>' }}
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{%- else -%}
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<|observation|>{% for tr in m.content %}
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<tool_response>{{ tr.output if tr.output is defined else tr }}</tool_response>{% endfor -%}
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{% endif -%}
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{%- elif m.role == 'system' -%}
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<|system|>{{ visible_text(m.content) }}
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{%- endif -%}
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{%- endfor -%}
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{%- if add_generation_prompt -%}
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<|assistant|>{{- '</think>' if (enable_thinking is defined and not enable_thinking) else '<think>' -}}
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{%- endif -%}
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