Instructions to use AutomatosX/AX-gpt-oss-120b-MLX-AXQ-6bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AutomatosX/AX-gpt-oss-120b-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-gpt-oss-120b-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

How to use AutomatosX/AX-gpt-oss-120b-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-gpt-oss-120b-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-gpt-oss-120b-MLX-AXQ-6bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use AutomatosX/AX-gpt-oss-120b-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-gpt-oss-120b-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-gpt-oss-120b-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-gpt-oss-120b-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-gpt-oss-120b-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-gpt-oss-120b-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-gpt-oss-120b-MLX-AXQ-6bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use AutomatosX/AX-gpt-oss-120b-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-gpt-oss-120b-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-gpt-oss-120b-MLX-AXQ-6bit

Run Hermes

hermes

Atomic Chat

AX-gpt-oss-120b-MLX-AXQ-6bit

AXQuant affine re-pack of mlx-community/gpt-oss-120b-MXFP4-Q4 for Apple Silicon MLX.

Property	Value
Product	AXQ 6-bit (agent-coding manual recipe, no 4-bit trunk)
Measured total BPW	6.577
Architecture	GptOssForCausalLM (MoE, no MTP)
Source	mlx-community/gpt-oss-120b-MXFP4-Q4@bce781bef0f2fc85ed4e575af74054f5aad73ddd
Upstream	openai/gpt-oss-120b
Plan	plan-manual agent-coding: experts 6-bit, attention 8-bit, no 4-bit
Runtime	MLX-LM

Checkpoint Tier 1

Certified on host df-macbookpro-m5 with AXQuant 1.6.1 development suites (agent-coding + general, seed 20260728, max tokens 64) vs the matched MXFP4-Q4 reference. MTP Tier 2 is not applicable (no MTP).

Load

```bash pip install mlx-lm python -m mlx_lm.generate --model AutomatosX/AX-gpt-oss-120b-MLX-AXQ-6bit --prompt Hello ```

Notes

Converted with AXQUANT_FORCE_CPU=1 after Metal GPU timeouts on large re-pack.
Size ratio vs MXFP4-Q4 is ~1.54 (within the 6-bit max 1.55 gate).
Higher-fidelity 6-bit product layout (storage-adjusted BPW ~6.58), not uniform 6.0.

Modalities (capability-gated)

Text checkpoint Tier 1 does not imply vision or audio quality. Vision present=true on a pack is not a quality pass.

Modality	Claim	Supported	Reason
Vision	`not-applicable`	`false`	vision not supported (no tower config and no sidecar weights)
Audio	`not-applicable`	`false`	audio not supported (no tower config and no sidecar weights)

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Safetensors

Model size

26B params

Tensor type

BF16

U32

MLX

Hardware compatibility

6-bit

Model tree for AutomatosX/AX-gpt-oss-120b-MLX-AXQ-6bit

Base model

openai/gpt-oss-120b

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