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

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

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"

AX-Nemotron-3.5-Lightning-30B-A3B-MLX-AXQ-MXFP4-MTP / axquant_mtp_sidecar_manifest.json

AutomatosX

Publish AX-Nemotron-3.5-Lightning-30B-A3B-MLX-AXQ-MXFP4-MTP development checkpoint

5ab1716 verified 9 days ago

Raw History Blame

916 Bytes

	{
	"created_at": "2026-10-04T05:39:06.057376Z",
	"dtypes": [
	"BF16",
	"F32"
	],
	"output": {
	"path": "mtp.safetensors",
	"sha256": "40dd606b285acd5045bd4246a916463eeb1d5f4cc7a4ebe051555985ec436e72",
	"size_bytes": 2670685376
	},
	"parameters": 1335325952,
	"role": "mtp",
	"schema_version": "axquant.protected-tensor-sidecar.v1",
	"source_files": [
	{
	"path": "model-00014-of-00014.safetensors",
	"sha256": "64577b275ca4e7e5266eae0903674f7f46ec2a8cbf4f4f1a3207f80d503cd1d0",
	"size_bytes": 2670685240
	}
	],
	"source_model": {
	"architecture": "NemotronHForCausalLM",
	"format": "mlx",
	"local_path": null,
	"model_id": "nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16",
	"revision": "a9904d24bcc1d289a1950fa9d2b978c47cf903b9"
	},
	"tensor_count": 270,
	"tensor_names_sha256": "8183b42de4bb62dfd622dc778f788a5069cf76ca700bc119e69dccb89738da1a"
	}