Instructions to use jedisct1/Nex-N2-mini-mlx-OptiQ-4bit-MTP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use jedisct1/Nex-N2-mini-mlx-OptiQ-4bit-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("jedisct1/Nex-N2-mini-mlx-OptiQ-4bit-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
- Pi
How to use jedisct1/Nex-N2-mini-mlx-OptiQ-4bit-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 "jedisct1/Nex-N2-mini-mlx-OptiQ-4bit-MTP"
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-OptiQ-4bit-MTP" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use jedisct1/Nex-N2-mini-mlx-OptiQ-4bit-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 "jedisct1/Nex-N2-mini-mlx-OptiQ-4bit-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 jedisct1/Nex-N2-mini-mlx-OptiQ-4bit-MTP
Run Hermes
hermes
- MLX LM
How to use jedisct1/Nex-N2-mini-mlx-OptiQ-4bit-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 "jedisct1/Nex-N2-mini-mlx-OptiQ-4bit-MTP"
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-OptiQ-4bit-MTP" # 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-OptiQ-4bit-MTP", "messages": [ {"role": "user", "content": "Hello"} ] }'
Nex-N2-mini, OptiQ 4-bit MLX, native MTP
This is nex-agi/Nex-N2-mini converted to MLX format, quantized with oMLX's oQ4 mixed-precision scheme, and extended with a multi-token prediction head for native MTP speculative decoding in oMLX (enable the Native MTP toggle in the model settings).
About the MTP head: the released Nex-N2-mini checkpoint declares an MTP layer in its config but does not ship the weights. Since Nex-N2-mini is post-trained from Qwen3.5-35B-A3B-Base and keeps identical dimensions, this conversion grafts the MTP head from Qwen3.5-35B-A3B onto the Nex-N2-mini trunk. MTP drafts are always verified by the main model, so outputs are exactly what the trunk would produce on its own; the head only affects speed. In practice the grafted head reaches around 40-50% draft acceptance on prose and reasoning, less on unusual token sequences, for up to roughly 1.5 tokens per backbone pass.
Note that the preserved mtp tensors confuse the weight-name heuristics of plain mlx-lm, so this repo is intended for oMLX. For mlx-lm, use jedisct1/Nex-N2-mini-mlx-OptiQ-4bit instead, which is the same quantization without the head.
The original vision tower is not included; this copy is text-only.
Tool calling works without any extra configuration, including through the MTP decode
path. The chat template uses the Qwen3-Coder XML style, which oMLX detects
automatically, so the server returns 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.
An 8-bit companion with the same MTP head is available at jedisct1/Nex-N2-mini-mlx-OptiQ-8bit-MTP.
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Model tree for jedisct1/Nex-N2-mini-mlx-OptiQ-4bit-MTP
Base model
nex-agi/Nex-N2-mini