inferencerlabs/Mistral-Medium-3.5-MLX-Q9
See Mistral-Medium-3.5-128B in action - [demonstration videos](https://youtube.com/xcreate)
Tested on a M3 Ultra 512GB RAM using Inferencer app v1.11.5
- Text inference ~5 tokens/s
- Vision inference ~4.5 tokens/s
- Memory usage: ~138 GiB
<p style="margin-bottom:0px;"> <strong>Q9 typically achieves near lossless accuracy in our coding test</strong>
</p> <table style="border-collapse: collapse; text-align:center; margin-top:10px; margin-bottom:0px;"> <thead> <tr><th>Quantization</th><th>Perplexity</th><th>Token Accuracy</th><th>Missed Divergence</th></tr> </thead> <tbody> <tr><td><strong>q3.5</strong></td><td>168.0</td><td>43.45%</td><td>72.57%</td></tr> <tr><td><strong>q4.5</strong></td><td>1.33593</td><td>91.65%</td><td>27.61%</td></tr> <tr><td><strong>q4.8</strong></td><td>1.28125</td><td>93.75%</td><td>21.15%</td></tr> <tr><td><strong>q5.5</strong></td><td>1.23437</td><td>95.05%</td><td>17.28%</td></tr> <tr><td><strong>q6.5</strong></td><td>1.21875</td><td>96.95%</td><td>12.03%</td></tr> <tr><td><strong>q8.5</strong></td><td>1.21093</td><td>97.55%</td><td>10.50%</td></tr> <tr><td><strong>q9</strong></td><td>1.21093</td><td>97.55%</td><td>10.50%</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
For more details see demonstration videos or visit Mistral-Medium-3.5-128B.
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.