Instructions to use Vontra/Nex-N2.5-mini-MLX-oQ2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Vontra/Nex-N2.5-mini-MLX-oQ2 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("Vontra/Nex-N2.5-mini-MLX-oQ2")
config = load_config("Vontra/Nex-N2.5-mini-MLX-oQ2")

# 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 Vontra/Nex-N2.5-mini-MLX-oQ2 with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Vontra/Nex-N2.5-mini-MLX-oQ2"

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": "Vontra/Nex-N2.5-mini-MLX-oQ2"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use Vontra/Nex-N2.5-mini-MLX-oQ2 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 "Vontra/Nex-N2.5-mini-MLX-oQ2"

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 Vontra/Nex-N2.5-mini-MLX-oQ2

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Vontra/Nex-N2.5-mini-MLX-oQ2 with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Vontra/Nex-N2.5-mini-MLX-oQ2"

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 "Vontra/Nex-N2.5-mini-MLX-oQ2" \
  --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 · MLX oQ2

A community mixed-precision conversion by Vontra for Apple Silicon.

Model

Converted from the BF16 Nex-N2.5-mini checkpoint using oMLX oQ2. The oQ label is a target, not a claim that every tensor uses the same precision; see the per-module quantisation entries in config.json. This text-and-vision model uses the qwen3_5_moe architecture. The inspected BF16 checkpoint contained no matching MTP tensors, despite its configuration declaring one MTP layer. This release does not provide tested MTP decoding; keep MTP disabled.

Download and use

hf download Vontra/Nex-N2.5-mini-MLX-oQ2 --local-dir ./Nex-N2.5-mini-MLX-oQ2

Add the folder to oMLX model directories, refresh the model list and select it. Basic inference was tested with oMLX 0.6.4.

Use the upstream-recommended sampling:

{
  "temperature": 0.7,
  "top_p": 0.95,
  "top_k": 40
}

Set these explicitly in your client or model settings. Our sampled oQ2 retests also used reasoning_effort="none" and max_tokens=4096; these are test conditions, not an upstream recommendation to disable reasoning. Avoid greedy decoding for oQ2, which reproduced a repetition loop in our tests.

Validation and limitations

Tested on 9 September 2026 through oMLX 0.6.4 on an Apple Silicon Studio with 256 GiB unified memory. Exact arithmetic, a forced weather-tool call with a Paris argument, and identification of a synthetic red image passed for this quant. Tool calls were checked for formatting, not executed. These are basic checks, not a full coding, vision or agent evaluation.

Greedy decoding reproduced severe repetition. With recommended sampling, six of seven requests finished naturally; one coding response hit the cap and contained a runtime error, and a story missed its requested length. This aggressive quant is experimental, not reliability-certified.

Five sampled coding requests delivered approximately 100–101 output tokens per second over API elapsed time. These are end-to-end observations, not controlled decode-only benchmarks; cache state was not controlled. Peak request memory and context-fit limits have not been measured, so no Mac memory-tier recommendation is claimed. Long-context, multi-turn and broader vision quality remain unverified.

Short test

With the sampling settings above, try:

What is 17 multiplied by 19? Answer with only the number.

The recorded arithmetic check returned 323 with temperature=0 and reasoning_effort="none". A short correct response does not establish long-generation reliability.

Licence and attribution

The upstream repository declares Apache-2.0. Model training, architecture and the original Nex logo belong to Nex-AGI and the respective upstream contributors. This is an independent community conversion, not an official Nex-AGI release. Upstream benchmark scores are not evaluations of this quant.

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