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"
File size: 3,942 Bytes
77ceb4c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 | {%- 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 -%}
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