Instructions to use Vontra/Nex-N2.5-mini-MLX-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Vontra/Nex-N2.5-mini-MLX-8bit 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-8bit")
config = load_config("Vontra/Nex-N2.5-mini-MLX-8bit")

# 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-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 "Vontra/Nex-N2.5-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": "Vontra/Nex-N2.5-mini-MLX-8bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use Vontra/Nex-N2.5-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 "Vontra/Nex-N2.5-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 Vontra/Nex-N2.5-mini-MLX-8bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Vontra/Nex-N2.5-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 "Vontra/Nex-N2.5-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 "Vontra/Nex-N2.5-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-N2.5 mini · MLX 8bit

A community standard MLX conversion by Vontra for Apple Silicon.

Model

Converted from the BF16 Nex-N2.5-mini checkpoint using MLX-VLM affine quantisation with 8-bit weights and group size 64. Not every tensor is quantised: the converter reported 8.596 average bits per weight, and multimodal modules excluded by its default predicate retain higher precision. 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-8bit --local-dir ./Nex-N2.5-mini-MLX-8bit

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.

Four coding requests were tested: two finished naturally and two hit the 4096-token cap. No repeated-12-word sequence occurred more than twice. Complete code blocks were syntax-checked, not executed; incomplete responses and code correctness remain limitations. The structured JSON test passed. The story finished at 884 words, within the requested 700–900.

The four coding requests delivered approximately 77–81 output tokens per API elapsed second. This is end-to-end throughput, not a controlled decode-only benchmark; cache state was not controlled and HF uploads overlapped some tests. 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 using the recommended sampling above 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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