Text Generation
MLX
Safetensors
xing4_0
quantization
apple-silicon
Mixture of Experts
mla
hyper-connections
xing
telechat
base_model_size:10B to 100B
conversational
custom_code
4-bit precision
Instructions to use TokenAI-zer/Xing4.0-29B-A4B-4bit-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use TokenAI-zer/Xing4.0-29B-A4B-4bit-MLX 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("TokenAI-zer/Xing4.0-29B-A4B-4bit-MLX") 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
- Pi
How to use TokenAI-zer/Xing4.0-29B-A4B-4bit-MLX with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "TokenAI-zer/Xing4.0-29B-A4B-4bit-MLX"
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": "TokenAI-zer/Xing4.0-29B-A4B-4bit-MLX" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use TokenAI-zer/Xing4.0-29B-A4B-4bit-MLX with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "TokenAI-zer/Xing4.0-29B-A4B-4bit-MLX"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "TokenAI-zer/Xing4.0-29B-A4B-4bit-MLX" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TokenAI-zer/Xing4.0-29B-A4B-4bit-MLX", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use TokenAI-zer/Xing4.0-29B-A4B-4bit-MLX 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 "TokenAI-zer/Xing4.0-29B-A4B-4bit-MLX"
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 TokenAI-zer/Xing4.0-29B-A4B-4bit-MLX
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use TokenAI-zer/Xing4.0-29B-A4B-4bit-MLX with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "TokenAI-zer/Xing4.0-29B-A4B-4bit-MLX"
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 "TokenAI-zer/Xing4.0-29B-A4B-4bit-MLX" \ --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"
| {%- 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 -%} | |
| {%- if messages[0]["role"] == "system" %} | |
| {%- set system_message = messages[0]["content"] %} | |
| {%- set loop_messages = messages[1:] %} | |
| {%- else %} | |
| {%- set loop_messages = messages %} | |
| {%- endif %} | |
| {%- if not tools is defined %} | |
| {%- set tools = [] %} | |
| {%- endif %} | |
| {%- set default_system = "你是中国电信星辰语义大模型,英文名是Xing,你是由中电信人工智能科技有限公司研发的人工智能助手。\n" %} | |
| {%- if system_message is defined %} | |
| {{- "<_system>" + visible_text(system_message) }} | |
| {%- else %} | |
| {{- "<_system>" + default_system }} | |
| {%- endif %} | |
| {% if tools is iterable and tools | length > 0 %} | |
| # 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}<param_key>{param-key-1}</param_key><param_value>{param-value-1}</param_value><param_key>{param-key-2}</param_key><param_value>{param-value-2}</param_value>...</tool_call> | |
| {%- endif %} | |
| {%- set ns = namespace(last_user_index=-1) %} | |
| {%- for m in loop_messages %} | |
| {%- if m.role == 'user' %} | |
| {%- set ns.last_user_index = loop.index0 -%} | |
| {%- endif %} | |
| {%- endfor %} | |
| {%- for m in loop_messages %} | |
| {%- if m.role == 'user' -%}<_user>{{ visible_text(m.content) }} | |
| {%- elif m.role == 'assistant' or m.role == 'bot' -%} | |
| <_bot> | |
| {%- set reasoning_content = '' %} | |
| {%- set content = visible_text(m.content) %} | |
| {%- if m.reasoning_content is string %} | |
| {%- set reasoning_content = m.reasoning_content %} | |
| {%- elif m.reasoning is string %} | |
| {%- set reasoning_content = m.reasoning %} | |
| {%- 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 -%} | |
| {{ '</think>' }} | |
| {%- elif enable_thinking is not defined or enable_thinking -%} | |
| {{ '<think>\n' + reasoning_content.strip() + '\n</think>'}} | |
| {%- else -%} | |
| {{ '</think>' }} | |
| {%- endif -%} | |
| {%- if content.strip() -%} | |
| {{ content.strip() }} | |
| {%- endif -%} | |
| {% if m.tool_calls %} | |
| {%- for tc in m.tool_calls %} | |
| {%- if tc.function %} | |
| {%- set tc = tc.function %} | |
| {%- endif %} | |
| {{- '<tool_call>' + tc.name -}} | |
| {% set _args = tc.arguments %} | |
| {%- for k, v in _args.items() %}<param_key>{{ k }}</param_key><param_value>{{ v | tojson(ensure_ascii=False) if v is not string else v }}</param_value>{% endfor %}{{ '</tool_call>' }}{% endfor %}{% endif %}{{- '<_end>\n' }} | |
| {%- elif m.role == 'tool' -%} | |
| {%- if loop.index0 > 0 -%} | |
| {%- set prev_element = loop_messages[loop.index0 - 1] -%} | |
| {%- if prev_element.role != "tool" -%} | |
| {{- '<_observation>' -}} | |
| {%- endif -%} | |
| {%- endif -%} | |
| {%- if m.content is string -%} | |
| {{- '<tool_response>' -}} | |
| {{- m.content }} | |
| {{- '</tool_response>' -}} | |
| {%- else -%} | |
| {% 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 -%} | |
| <_bot>{{- '</think>' if (enable_thinking is defined and not enable_thinking) else '<think>\n' -}} | |
| {%- endif -%} | |