Instructions to use AutomatosX/AX-gemma-4-31b-MLX-AXQ-6bit-MTP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AutomatosX/AX-gemma-4-31b-MLX-AXQ-6bit-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-gemma-4-31b-MLX-AXQ-6bit-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-gemma-4-31b-MLX-AXQ-6bit-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-gemma-4-31b-MLX-AXQ-6bit-MTP"

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-gemma-4-31b-MLX-AXQ-6bit-MTP"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use AutomatosX/AX-gemma-4-31b-MLX-AXQ-6bit-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-gemma-4-31b-MLX-AXQ-6bit-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-gemma-4-31b-MLX-AXQ-6bit-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"

MLX LM

How to use AutomatosX/AX-gemma-4-31b-MLX-AXQ-6bit-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-gemma-4-31b-MLX-AXQ-6bit-MTP"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "AutomatosX/AX-gemma-4-31b-MLX-AXQ-6bit-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-gemma-4-31b-MLX-AXQ-6bit-MTP",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use AutomatosX/AX-gemma-4-31b-MLX-AXQ-6bit-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-gemma-4-31b-MLX-AXQ-6bit-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-gemma-4-31b-MLX-AXQ-6bit-MTP

Run Hermes

hermes

AX-gemma-4-31b-MLX-AXQ-6bit-MTP

Checkpoint Tier 1 certified on df-macbookpro-m5 (2026-08-09) for Hub commit f024707acb0d. Measured size vs matched uniform baseline, quality retention ≥0.98, and conversion integrity. Tier 1 is not a speed claim: MTP acceleration is not certified. See the certificate.

AXQuant mixed-precision (AXQ) Gemma 4 target with multi-token prediction (MTP) — named like the Qwen AXQ-MTP line (…-MLX-AXQ-*-MTP).

Property	Value
Product class	AXQ 6bit
Target size	31B
Measured BPW	~6.00
Hub id	`AutomatosX/AX-gemma-4-31b-MLX-AXQ-6bit-MTP`
Hub commit	`f024707acb0d123642a1ed21c75fc2b7b337bd1e`
Engine pair	`gemma-4-31b-it` ↔ `gemma-4-31b-it-assistant`
MTP layout	`assistant/` + `ax_gemma4_assistant_mtp.json` (Gemma assistant-MTP)
Certification host	`df-macbookpro-m5`

Layout

./                          # AXQ target weights (+ vision sidecar when present)
assistant/                  # gemma4_assistant drafter
ax_gemma4_assistant_mtp.json
ax_composite_pack_manifest.json
axquant_*.json

Naming follows Qwen AXQ MTP packs: the -MTP suffix means MTP assets are in-repo.

Certification

Claim	Status
Checkpoint Tier 1 (size + quality + load)	Certified on `df-macbookpro-m5`
MTP acceleration Tier 2	Not certified (assistant-MTP is present for product completeness only)
Vision / multimodal quality	Not claimed

Certificate: gemma4-31b-axq6-tier1.md

Runtime (AX Engine)

export AX_MLX_GEMMA4_ASSISTANT_MTP=1
export AX_MLX_GEMMA4_ASSISTANT_MTP_MAX_DEPTH=2
# load AutomatosX/AX-gemma-4-31b-MLX-AXQ-6bit-MTP as local snapshot root / --mlx-model-artifacts-dir

Default product text route remains direct decode unless you explicitly enable assistant-MTP.

Published / card updated: 2026-08-09.

Downloads last month: 6

Safetensors

Model size

6B params

Tensor type

BF16

U32

MLX

Hardware compatibility

4-bit

Model tree for AutomatosX/AX-gemma-4-31b-MLX-AXQ-6bit-MTP

Base model

google/gemma-4-31B

Finetuned

google/gemma-4-31B-it

Quantized

(299)

this model