Instructions to use stefanprodan/Apodex-1.1-mini-oQ4e-mtp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use stefanprodan/Apodex-1.1-mini-oQ4e-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("stefanprodan/Apodex-1.1-mini-oQ4e-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 stefanprodan/Apodex-1.1-mini-oQ4e-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 "stefanprodan/Apodex-1.1-mini-oQ4e-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": "stefanprodan/Apodex-1.1-mini-oQ4e-mtp"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use stefanprodan/Apodex-1.1-mini-oQ4e-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 "stefanprodan/Apodex-1.1-mini-oQ4e-mtp"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "stefanprodan/Apodex-1.1-mini-oQ4e-mtp"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "stefanprodan/Apodex-1.1-mini-oQ4e-mtp",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use stefanprodan/Apodex-1.1-mini-oQ4e-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 "stefanprodan/Apodex-1.1-mini-oQ4e-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 stefanprodan/Apodex-1.1-mini-oQ4e-mtp

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use stefanprodan/Apodex-1.1-mini-oQ4e-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 "stefanprodan/Apodex-1.1-mini-oQ4e-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 "stefanprodan/Apodex-1.1-mini-oQ4e-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"

Apodex-1.1-mini-oQ4e-mtp

A 4-bit mixed-precision MLX build of apodex/Apodex-1.1-mini (35B-A3B MoE, Qwen3.5 architecture) made with the oQ quantizer built into oMLX 0.6.4, for Apple Silicon. Unlike the plain 4-bit MLX conversions, the native Multi-Token Prediction head of the source checkpoint is kept, so oMLX can run Lightning MTP speculative decoding on it.

Quantization

Quantizer: oMLX oQ, level 4, enhanced (oQ4e), from the bf16 checkpoint
Group size: 64
Importance matrix: oMLX oqe_code_multilingual calibration set, 523 entries, no dead experts
Mixed precision: MoE experts at 4 bits; attention, shared expert and the MTP head at 6 to 8 bits (the exact per-tensor map is in config.json under quantization)
MTP head: preserved (language_model.mtp.*, 42 tensors)
Size on disk: 21.6 GB, 5 safetensors shards
Tokenizer, chat template and generation config are unchanged from the source

oq_imatrix_report.json documents the calibration run.

Usage

Built for and tested with oMLX 0.6.4 on an M2 Max. Download it from the oMLX model page by repo id (stefanprodan/Apodex-1.1-mini-oQ4e-mtp) or place the folder under ~/.omlx/models, then enable Lightning MTP in the model settings to use the MTP head. The chat template enables thinking by default; pass chat_template_kwargs: {"enable_thinking": false} to turn it off.

Other MLX runtimes have not been tested with this build.

License

Apache 2.0, inherited from the source model. See the Apodex-1.1-mini model card for the model description, evaluation results and the technical report.