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

Libraries

How to use abenzerps/Apodex-1.1-mini-MLX 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("abenzerps/Apodex-1.1-mini-MLX")

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 abenzerps/Apodex-1.1-mini-MLX with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "abenzerps/Apodex-1.1-mini-MLX"

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": "abenzerps/Apodex-1.1-mini-MLX"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use abenzerps/Apodex-1.1-mini-MLX with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "abenzerps/Apodex-1.1-mini-MLX"

Run an OpenAI-compatible server

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

Hermes Agent

How to use abenzerps/Apodex-1.1-mini-MLX 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 "abenzerps/Apodex-1.1-mini-MLX"

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 abenzerps/Apodex-1.1-mini-MLX

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use abenzerps/Apodex-1.1-mini-MLX with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "abenzerps/Apodex-1.1-mini-MLX"

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 "abenzerps/Apodex-1.1-mini-MLX" \
  --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 MLX

MLX conversion of apodex/Apodex-1.1-mini, a 35.95B-parameter Qwen3.5 MoE model designed for research, data, files, code, and tool-driven work.

This release is optimized for text generation with mlx-lm on Apple silicon.

Upstream benchmarks

Both figures are reproduced from the upstream model card. They report evaluations performed with FrontierAgent, Apodex's open-source agent runtime and evaluation suite, using its ReAct and Agent Team workflows. They do not measure this MLX conversion.

Upstream Apodex-1.1 evaluation results using FrontierAgent's ReAct and Agent Team workflows.

Upstream Apodex-1.1-mini evaluation results using FrontierAgent. With the Agent Team workflow, the model scored 27.7 on APEX-Agent, 50.2 on FrontierFinance, and 51.7 on FrontierScience-Research.

Model details

Item	Value
Format	MLX safetensors
Quantization	Affine 4-bit, group size 64
Effective bits per weight	4.503
Model size	19.5 GB
Non-quantized dtype	BF16
Architecture	`Qwen3_5MoeForConditionalGeneration`

The MoE router and shared-expert gate weights remain at 8-bit; other eligible weights use affine 4-bit quantization.

This is a text-only MLX release. Image input is not supported. Current official mlx-lm Qwen3.5 support filters the native mtp.* weights, so native MTP is not included. Standard generation is unaffected.

Hugging Face may show approximately 5B parameters because packed MLX uint32 tensors are counted as stored elements. The source model contains 35.95B parameters.

Upstream compatibility

The tokenizer configuration and chat template are synchronized with upstream revision 62583b4.

Usage

pip install -U mlx-lm

mlx_lm.generate \
  --model abenzerps/Apodex-1.1-mini-MLX \
  --prompt "Explain why reproducible builds matter." \
  --max-tokens 512 --temp 0.7

The 19.5 GB weights require additional unified memory for the runtime and KV cache, especially at long context lengths. To disable thinking, pass --chat-template-config '{"enable_thinking": false}'.

Checksums

SHA-256 hashes for every shard and supporting file are provided in SHA256SUMS.

sha256sum -c SHA256SUMS

License

The source model was released by Apodex AI under the Apache License 2.0. The same license and attribution requirements apply to this conversion.

Downloads last month: 12

Safetensors

Model size

5B params

Tensor type

BF16

U32

MLX

Hardware compatibility

4-bit

Model tree for abenzerps/Apodex-1.1-mini-MLX

Base model

Qwen/Qwen3.5-35B-A3B-Base

Finetuned

Qwen/Qwen3.5-35B-A3B

Finetuned

apodex/Apodex-1.1-mini

Quantized

(13)

this model