Instructions to use AutomatosX/AX-Nemotron-3.5-Lightning-30B-A3B-MLX-AXQ-MXFP4-MTP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use AutomatosX/AX-Nemotron-3.5-Lightning-30B-A3B-MLX-AXQ-MXFP4-MTP 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-Nemotron-3.5-Lightning-30B-A3B-MLX-AXQ-MXFP4-MTP") 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-Nemotron-3.5-Lightning-30B-A3B-MLX-AXQ-MXFP4-MTP 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-Nemotron-3.5-Lightning-30B-A3B-MLX-AXQ-MXFP4-MTP"
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": "AutomatosX/AX-Nemotron-3.5-Lightning-30B-A3B-MLX-AXQ-MXFP4-MTP" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use AutomatosX/AX-Nemotron-3.5-Lightning-30B-A3B-MLX-AXQ-MXFP4-MTP 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-Nemotron-3.5-Lightning-30B-A3B-MLX-AXQ-MXFP4-MTP"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "AutomatosX/AX-Nemotron-3.5-Lightning-30B-A3B-MLX-AXQ-MXFP4-MTP" # 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-Nemotron-3.5-Lightning-30B-A3B-MLX-AXQ-MXFP4-MTP", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use AutomatosX/AX-Nemotron-3.5-Lightning-30B-A3B-MLX-AXQ-MXFP4-MTP 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-Nemotron-3.5-Lightning-30B-A3B-MLX-AXQ-MXFP4-MTP"
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-Nemotron-3.5-Lightning-30B-A3B-MLX-AXQ-MXFP4-MTP
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use AutomatosX/AX-Nemotron-3.5-Lightning-30B-A3B-MLX-AXQ-MXFP4-MTP 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-Nemotron-3.5-Lightning-30B-A3B-MLX-AXQ-MXFP4-MTP"
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-Nemotron-3.5-Lightning-30B-A3B-MLX-AXQ-MXFP4-MTP" \ --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"
Runtime format audit (2026-10-06)
No quantization-container correction was needed. This family has no n-gram tensors; no n-gram file or declaration was added. This remote format audit does not grant an oMLX/MTPLX runtime profile or a successful load/generation claim.
See runtime_audit.json for pinned config/index/header bindings, architecture, physical-format findings, and applied corrections. This is development evidence; no quality, MTP exactness, speed, or certification claim is added. Historical evidence stays bound to its original revision.
AX-Nemotron-3.5-Lightning-30B-A3B-MLX-AXQ-MXFP4-MTP
This repository contains an AXQuant development conversion of the pinned NVIDIA BF16 source. The main checkpoint uses standard MLX-LM config, tokenizer, index, and safetensors files. MXFP quantization is applied to eligible backbone weights; protected tensors keep their declared precision.
Source and format
- Source: nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16
- Immutable source revision:
a9904d24bcc1d289a1950fa9d2b978c47cf903b9 - AXQuant physical format:
MXFP4 - AXQuant plan and file hashes are included in this repository.
- This is a development artifact; no quality certificate is claimed.
MTP payload
The source integrates Nemotron-H MTP tensors into its original checkpoint shards. AXQuant preserves those tensor payloads byte-for-byte in mtp.safetensors and records the source and payload digests in ax_nemotron_mtp_manifest.json. Runtime compatibility is unverified. This package does not claim MTP execution support in MLX-LM, AX Engine, MTPLX, or oMLX; each runtime owns that support.
Load the standard MLX backbone
from mlx_lm import load, generate
model, tokenizer = load("AutomatosX/AX-Nemotron-3.5-Lightning-30B-A3B-MLX-AXQ-MXFP4-MTP")
print(generate(model, tokenizer, prompt="Hello", max_tokens=32))
The example loads the text backbone. It does not load or activate the separate MTP payload. Super needs substantial memory; actual use depends on format, context, and runtime.
License and notices
The source uses OpenMDW License 1.1; its license and notices are included.
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