---
title: Nex-N2-Pro-MLX-Q9-archive
canonical_url: "https://www.modelscope.ai/models/inferencerlabs/Nex-N2-Pro-MLX-Q9-archive"
md_url: "https://www.modelscope.ai/models/inferencerlabs/Nex-N2-Pro-MLX-Q9-archive.md"
repository: inferencerlabs/Nex-N2-Pro-MLX-Q9-archive
last_updated: 2026-07-31
pipeline_tag: image-text-to-text
tasks:
  - image-text-to-text
model_type:
  - qwen3_5_moe
architectures:
  - Qwen3_5MoeForConditionalGeneration
base_model:
  - nex-agi/Nex-N2-Pro
base_model_relation: quantized
parameters: 124.3B
tensor_type:
  - BF16
  - U32
library_name:
  - mlx
  - safetensors
  - pytorch
frameworks:
  - pytorch
language:
  - en
downloads: 75
stars: 0
tags:
  - quantized
  - mlx
---

# Nex-N2-Pro-MLX-Q9-archive

> Nex-N2-Pro-MLX-Q9-archive - An open-source model by inferencerlabs on ModelScope. Nex-N2-Pro-9bit See Nex-N2-Pro in action: demonstration videos Tested with an M3 Ultra 512 GiB using Inferencer app v1.11.7 Text Inference: 25.3 tokens/s @ 1000 tokens 415.4…

inferencerlabs/Nex-N2-Pro-MLX-Q9-archive is a 124.3B-parameter image-text-to-text model on ModelScope. derived from nex-agi/Nex-N2-Pro.

- **Repository**: inferencerlabs/Nex-N2-Pro-MLX-Q9-archive
- **Tasks**: image-text-to-text
- **Parameters**: 124.3B
- **Base model**: nex-agi/Nex-N2-Pro
- **Tags**: quantized, mlx
- **Downloads**: 75
- **Stars**: 0
- **Last updated**: 2026-07-31

Source: https://www.modelscope.ai/models/inferencerlabs/Nex-N2-Pro-MLX-Q9-archive

---

# Nex-N2-Pro-9bit
See Nex-N2-Pro in action: [demonstration videos](https://youtube.com/xcreate)
   
#### Tested with an M3 Ultra 512 GiB using [Inferencer app v1.11.7](https://inferencer.com)
- Text Inference: ~25.3 tokens/s @ 1000 tokens ~415.4 GiB
- Vision Inference: ~23.4 tokens/s ~416.2 GiB

<p style="margin-bottom:0px;">
<strong>Q9 typically achieves near lossless accuracy in our coding test</strong>

![Screenshot](https://cdn-uploads.huggingface.co/production/uploads/688479d616f1ec82fa645019/OVthaWqLxak7_7ddLgZ2W.jpeg)
</p>
<table style="border-collapse: collapse; text-align:center; margin-top:10px; margin-bottom:0px;">
<thead>
<tr><th>Quantization (bpw)</th><th>Perplexity</th><th>Token Accuracy</th><th>Missed Divergence</th></tr>
</thead>
<tbody>
<tr><td><strong>q4.5</strong></td><td>1.32812</td><td>90.5%</td><td>26.44%</td></tr>
<tr><td><strong>q5.5</strong></td><td>1.23437</td><td>95.4%</td><td>16.03%</td></tr>
<tr><td><strong>q6.5</strong></td><td>1.21875</td><td>96.85%</td><td>12.55%</td></tr>
<tr><td><strong>q8.5</strong></td><td>1.21875</td><td>97.65%</td><td>9.92%</td></tr>
<tr><td><strong>q9</strong></td><td>1.21093</td><td>97.95%</td><td>9.61%</td></tr>
<tr><td><strong>Base</strong></td><td>1.20312</td><td>100.0%</td><td>0.000%</td></tr>
</tbody>
</table>

<ul>
<li style="margin:0;">Perplexity: Measures the confidence for predicting base tokens (lower is better)</li>
<li style="margin:0;">Token Accuracy: The percentage of correctly generated base tokens</li>
<li style="margin:0;">Missed Divergence: Measures severity of misses; how much the token was missed by</li>
</ul>

##### Quantized with a modified version of [MLX](https://github.com/ml-explore/mlx)
##### For more details see our [demonstration videos](https://youtube.com/xcreate) or visit [Nex-N2-Pro](https://huggingface.co/nex-agi/Nex-N2-Pro).

## Disclaimer

We are not the creator, originator, or owner of any model listed. Each model is created and provided by third parties. Models may not always be accurate or contextually appropriate. You are responsible for verifying the information before making important decisions. We are not liable for any damages, losses, or issues arising from its use, including data loss or inaccuracies in AI-generated content.