Instructions to use spicyneuron/Nex-N2-Pro-MLX-5.3bit-vision with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use spicyneuron/Nex-N2-Pro-MLX-5.3bit-vision 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("spicyneuron/Nex-N2-Pro-MLX-5.3bit-vision")

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 spicyneuron/Nex-N2-Pro-MLX-5.3bit-vision with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "spicyneuron/Nex-N2-Pro-MLX-5.3bit-vision"

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": "spicyneuron/Nex-N2-Pro-MLX-5.3bit-vision"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use spicyneuron/Nex-N2-Pro-MLX-5.3bit-vision 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 "spicyneuron/Nex-N2-Pro-MLX-5.3bit-vision"

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 spicyneuron/Nex-N2-Pro-MLX-5.3bit-vision

Run Hermes

hermes

OpenClaw new

How to use spicyneuron/Nex-N2-Pro-MLX-5.3bit-vision with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "spicyneuron/Nex-N2-Pro-MLX-5.3bit-vision"

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 "spicyneuron/Nex-N2-Pro-MLX-5.3bit-vision" \
  --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 spicyneuron/Nex-N2-Pro-MLX-5.3bit-vision with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "spicyneuron/Nex-N2-Pro-MLX-5.3bit-vision"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "spicyneuron/Nex-N2-Pro-MLX-5.3bit-vision"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "spicyneuron/Nex-N2-Pro-MLX-5.3bit-vision",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Nex-N2-Pro-MLX-5.3bit-vision / README.md

spicyneuron

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	---
	license: apache-2.0
	pipeline_tag: text-generation
	library_name: mlx
	tags:
	- mlx
	base_model: nex-agi/Nex-N2-Pro
	---

	[Nex-N2-Pro](https://huggingface.co/nex-agi/Nex-N2-Pro) optimized for MLX.
	This is one of the best coding models that runs on a Mac Studio!

	- A mixed-precision quant that balances speed, memory, and accuracy.
	- 4-bit baseline with important layers at higher precision.
	- Supports image input and requires a vision-capable MLX server.

	# Usage

	```sh
	# Start server at http://localhost:8080/v1/chat/completions
	uvx --from mlx-vlm mlx_vlm.server \
	--host 127.0.0.1 \
	--port 8080 \
	--model spicyneuron/Nex-N2-Pro-MLX-5.3bit-vision
	```

	# Benchmarks

	Tested on a Mac Studio M3 Ultra.

	metric \| this model
	--- \| ---
	bpw \| 5.349
	base memory \| 246.796
	peak memory (1024/512) \| 267.043
	prompt tok/s (1024) \| 475.490 ± 0.195
	gen tok/s (512) \| 30.802 ± 0.154
	kl mean\* \| 0.012 ± 0.001
	kl p95\* \| 0.029 ± 0.001
	perplexity \| 3.677 ± 0.023
	ifbench_strict \| 0.470 ± 0.050
	ifbench_loose \| 0.520 ± 0.050
	arc_challenge \| 0.696 ± 0.021
	hellaswag \| 0.922 ± 0.012

	\*KL was measured against the largest quant I could run (~495GB), so real value is higher.

	# Methodology

	Quantized with a [mlx-vlm fork](https://github.com/spicyneuron/mlx-vlm/tree/override).
	MLX quantization options differ than llama.cpp, but the principles are the same:

	- Sensitive layers like MoE routing, attention, and output embeddings get higher precision
	- More tolerant layers like MoE experts get lower precision

	Related tooling:

	- [Benchmark VLMs with `mlx_lm`](https://github.com/ml-explore/mlx-lm/pull/1033)
	- [`mlx_lm.kld` command](https://github.com/ml-explore/mlx-lm/pull/1146)