Instructions to use TokenAI-zer/Nex-N2.5-mini-oQ5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TokenAI-zer/Nex-N2.5-mini-oQ5 with MLX:

# Make sure mlx-vlm is installed
# pip install --upgrade mlx-vlm

from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config

# Load the model
model, processor = load("TokenAI-zer/Nex-N2.5-mini-oQ5")
config = load_config("TokenAI-zer/Nex-N2.5-mini-oQ5")

# Prepare input
image = ["http://images.cocodataset.org/val2017/000000039769.jpg"]
prompt = "Describe this image."

# Apply chat template
formatted_prompt = apply_chat_template(
    processor, config, prompt, num_images=1
)

# Generate output
output = generate(model, processor, formatted_prompt, image)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use TokenAI-zer/Nex-N2.5-mini-oQ5 with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "TokenAI-zer/Nex-N2.5-mini-oQ5"

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": "TokenAI-zer/Nex-N2.5-mini-oQ5"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use TokenAI-zer/Nex-N2.5-mini-oQ5 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 "TokenAI-zer/Nex-N2.5-mini-oQ5"

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 TokenAI-zer/Nex-N2.5-mini-oQ5

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use TokenAI-zer/Nex-N2.5-mini-oQ5 with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "TokenAI-zer/Nex-N2.5-mini-oQ5"

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 "TokenAI-zer/Nex-N2.5-mini-oQ5" \
  --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-N2.5-mini-oQ5 / README.md

TokenAI-zer

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	---
	base_model: nex-agi/Nex-N2.5-mini
	model_name: Nex-N2.5-mini-oQ5
	library_name: mlx
	pipeline_tag: image-text-to-text
	license: apache-2.0
	tags:
	- mlx
	- omlx
	- quantization
	- mixed-precision
	- apple-silicon
	- moe
	- vision
	- base_model:quantized:nex-agi/Nex-N2.5-mini
	---

	# Nex-N2.5-mini-oQ5

	Unofficial MLX quantization of [nex-agi/Nex-N2.5-mini](https://huggingface.co/nex-agi/Nex-N2.5-mini) for Apple Silicon. The upstream model is a multimodal mixture-of-experts model; this repository contains MLX safetensors, not GGUF or PyTorch weights. I am not affiliated with Nex AGI.

	## What is in this repository

	\| Property \| Value \|
	\|---\|---\|
	\| Architecture \| Qwen3_5MoeForConditionalGeneration \|
	\| Quantization \| affine, group size 64; 5-bit default with 6-bit and 8-bit module overrides \|
	\| Weight size \| 23.57 GiB (25.30 GB), 5 safetensors shards \|
	\| Text model \| 40 layers, 256 experts, 8 experts selected per token \|
	\| Vision \| vision tensors retained in BF16 \|
	\| Context limit in config \| 262,144 tokens; usable context depends on available memory \|
	\| MTP \| not present (mtp_num_hidden_layers: 0) \|

	The numbers above were read from the shipped config.json, model.safetensors.index.json and safetensors headers. The five shards contain 2,010 indexed tensors. The quantization recipe is recorded in config.json so a compatible MLX loader can reconstruct the per-module precision.

	This conversion has not been benchmarked against the upstream BF16 model. The upstream benchmark figures on its [model card](https://huggingface.co/nex-agi/Nex-N2.5-mini) are not results for these quantized weights. Quantization can change output quality, and memory use grows with context length and cache settings.

	## Usage

	Download the model into your oMLX model directory:

	~~~bash
	hf download TokenAI-zer/Nex-N2.5-mini-oQ5 --local-dir ~/.omlx/models/Nex-N2.5-mini-oQ5
	omlx serve --model-dir ~/.omlx/models --port 8000
	~~~

	Use the model ID Nex-N2.5-mini-oQ5 in oMLX. This architecture includes a vision tower, so use an MLX runtime with Qwen3.5 MoE multimodal support. The 262k context value is an architecture limit, not a promise that it will fit in memory.

	## License and attribution

	The [upstream repository](https://huggingface.co/nex-agi/Nex-N2.5-mini) declares Apache License 2.0. The [published model](https://huggingface.co/TokenAI-zer/Nex-N2.5-mini-oQ5) includes the [license text](LICENSE) and a [notice](https://huggingface.co/TokenAI-zer/Nex-N2.5-mini-oQ5/blob/main/NOTICE) identifying the source and the quantization change. The upstream model and its reported evaluations belong to Nex AGI.

	## Citation

	~~~bibtex
	@misc{nex-n25-mini-oq5,
	title = {Nex-N2.5-mini-oQ5: MLX quantization of Nex-N2.5-mini},
	author = {TokenAI-zer},
	year = {2026},
	url = {https://huggingface.co/TokenAI-zer/Nex-N2.5-mini-oQ5},
	note = {Unofficial quantization of nex-agi/Nex-N2.5-mini}
	}
	~~~