Instructions to use jedisct1/Nex-N2-mini-mlx-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jedisct1/Nex-N2-mini-mlx-8bit 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("jedisct1/Nex-N2-mini-mlx-8bit")

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 jedisct1/Nex-N2-mini-mlx-8bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "jedisct1/Nex-N2-mini-mlx-8bit"

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": "jedisct1/Nex-N2-mini-mlx-8bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use jedisct1/Nex-N2-mini-mlx-8bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "jedisct1/Nex-N2-mini-mlx-8bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "jedisct1/Nex-N2-mini-mlx-8bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "jedisct1/Nex-N2-mini-mlx-8bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use jedisct1/Nex-N2-mini-mlx-8bit 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 "jedisct1/Nex-N2-mini-mlx-8bit"

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 jedisct1/Nex-N2-mini-mlx-8bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use jedisct1/Nex-N2-mini-mlx-8bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "jedisct1/Nex-N2-mini-mlx-8bit"

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 "jedisct1/Nex-N2-mini-mlx-8bit" \
  --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"

Nex-N2-mini-mlx-8bit / README.md

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

	# Nex-N2-mini, 8-bit MLX

	This is [nex-agi/Nex-N2-mini](https://huggingface.co/nex-agi/Nex-N2-mini) converted
	to MLX format and quantized to 8 bits (group size 64) with mlx-lm 0.31.3.

	Nex-N2-mini is an agentic model built around what its authors call Agentic Thinking:
	it interleaves reasoning, tool use, and environment feedback rather than treating them
	as separate stages. The architecture is a hybrid MoE (qwen3_5_moe): 40 layers
	alternating linear attention with full attention every fourth layer, 256 experts with
	8 active per token, and a 262k-token context window.

	The original checkpoint includes a vision tower. MLX text inference does not use it,
	so the vision weights were dropped during conversion; this copy is text-only. Expect
	roughly 37 GB of memory in use during inference.

	## Usage

	With mlx-lm, either directly:

	```sh
	mlx_lm.generate --model jedisct1/Nex-N2-mini-mlx-8bit --prompt "Hello"
	```

	or as an OpenAI-compatible server:

	```sh
	mlx_lm.server --model jedisct1/Nex-N2-mini-mlx-8bit
	```

	It also works out of the box with oMLX.

	Tool calling works without any extra configuration. The chat template uses the
	Qwen3-Coder XML style, which mlx-lm and oMLX both detect automatically, so servers
	return proper structured `tool_calls`, and thinking ends up in the reasoning field
	instead of leaking into the response content. Tested end to end with
	[Swival](https://swival.dev/) as the harness, including multi-step tasks that
	exercise file edits, search, and shell commands while the model is thinking.