---
language: en
library_name: mlx
tags:
- quantized
- mlx
base_model: MiniMaxAI/MiniMax-M3
base_model_relation: quantized
pipeline_tag: image-text-to-text
---
# MiniMax-M3

See MiniMax-M3 in action: [demonstration videos](https://youtube.com/xcreate)
   
#### Tested with an M3 Ultra 512 GiB using [Inferencer app v2.0.5](https://inferencer.com)
- Multimodal Inference: ~19 tokens/s @ 1000 tokens ~422 GiB

![Screenshot](https://cdn-uploads.huggingface.co/production/uploads/688479d616f1ec82fa645019/h6jHWOSeNhATMlqtE7o-k.jpeg)

<p style="margin-bottom:0px;">
<strong>Q8.5 typically achieves near lossless quality in our coding test.</strong>
</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>1.35937</td><td>89.75%</td><td>28.98%</td></tr>
<tr><td><strong>Q5.5</strong></td><td>1.24218</td><td>94.60%</td><td>17.55%</td></tr>
<tr><td><strong>Q6.5</strong></td><td>1.21875</td><td>96.85%</td><td>16.03%</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>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 [zai-org/GLM-5.1](https://huggingface.co/zai-org/GLM-5.2).

## 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.