Instructions to use jedisct1/Nex-N2-mini-mlx-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
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
- Pi
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 @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": "jedisct1/Nex-N2-mini-mlx-8bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
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
- 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"} ] }'
Nex-N2-mini, 8-bit MLX
This is 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:
mlx_lm.generate --model jedisct1/Nex-N2-mini-mlx-8bit --prompt "Hello"
or as an OpenAI-compatible server:
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 as the harness, including multi-step tasks that
exercise file edits, search, and shell commands while the model is thinking.
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Model tree for jedisct1/Nex-N2-mini-mlx-8bit
Base model
nex-agi/Nex-N2-mini