Text Generation
MLX
Safetensors
llama
apple-silicon
quantized
mixed-precision
axquant
axq
development
minicpm5
6bit
6-bit
conversational
4-bit precision
Instructions to use AutomatosX/AX-MiniCPM5-1B-MLX-AXQ-6bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use AutomatosX/AX-MiniCPM5-1B-MLX-AXQ-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("AutomatosX/AX-MiniCPM5-1B-MLX-AXQ-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
- Pi
How to use AutomatosX/AX-MiniCPM5-1B-MLX-AXQ-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 "AutomatosX/AX-MiniCPM5-1B-MLX-AXQ-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": "AutomatosX/AX-MiniCPM5-1B-MLX-AXQ-6bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use AutomatosX/AX-MiniCPM5-1B-MLX-AXQ-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 "AutomatosX/AX-MiniCPM5-1B-MLX-AXQ-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 AutomatosX/AX-MiniCPM5-1B-MLX-AXQ-6bit
Run Hermes
hermes
- OpenClaw new
How to use AutomatosX/AX-MiniCPM5-1B-MLX-AXQ-6bit with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "AutomatosX/AX-MiniCPM5-1B-MLX-AXQ-6bit"
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 "AutomatosX/AX-MiniCPM5-1B-MLX-AXQ-6bit" \ --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"
- MLX LM
How to use AutomatosX/AX-MiniCPM5-1B-MLX-AXQ-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 "AutomatosX/AX-MiniCPM5-1B-MLX-AXQ-6bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "AutomatosX/AX-MiniCPM5-1B-MLX-AXQ-6bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AutomatosX/AX-MiniCPM5-1B-MLX-AXQ-6bit", "messages": [ {"role": "user", "content": "Hello"} ] }'
File size: 1,643 Bytes
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"ax_engine": {
"decode_kernel": null,
"fused_mtp": null,
"kernel_evidence": "unmeasured",
"model_manifest": "model-manifest.json",
"preferred_group_size": 32
},
"compatible_runtimes": [
{
"compatibility_level": "B",
"manifest": "config.json",
"mtp_support": "runtime-dependent",
"name": "mlx-lm",
"notes": [
"Standard backbone inference is the compatibility target.",
"AXQuant MTP metadata may be ignored by MLX-LM."
],
"standard_inference": true,
"standard_mlx_weights": true,
"support_level": "standard-inference"
}
],
"created_at": "2026-08-02T18:28:18.657152Z",
"kv_cache": null,
"memory_policy": {
"kv_cache_precision": "runtime-default",
"mtp_buffers": "not-required",
"prefix_cache": "runtime-managed",
"unified_memory_safety_margin": "benchmark-required"
},
"mtp": {
"acceptance_retention": null,
"detected": false,
"draft_tokens": null,
"enabled_by_default": false,
"head_precision": null,
"measured_speedup": null,
"optimized": false,
"recommended_temperature_max": null,
"sidecar_file": null,
"verification_mode": null
},
"optimization_scope": "text-path",
"primary_runtime": {
"compatibility_level": "A",
"manifest": "model-manifest.json",
"mtp_support": "native",
"name": "ax-engine",
"notes": [
"Runtime claims require a passing AX Engine doctor and benchmark report."
],
"standard_inference": true,
"standard_mlx_weights": true,
"support_level": "optimized"
},
"schema_version": "axquant.runtime.v1"
}
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