inferencerlabs/Qwen3.8-Flash-Next-MLX-Q4
Qwen3.8-Flash-Next
See Qwen3.8-Flash-Next in action: demonstration videos
Tested on a M5 Max using Inferencer app
<table style="border-collapse: collapse; border: none; text-align:left; margin-top:10px; margin-bottom:0px;"> <tr><td><strong>Multimodal inference</strong></td><td>~57.5 tokens/s ~98.7 GiB</td></tr> </table>
For larger context windows consider increasing the available RAM for inferencing with: ``sudo sysctl iogpu.wired_limit_mb=118000``
<p style="margin-bottom:0px;"> <strong>Q4.5-bit quant typically achieves over 90% accuracy.</strong> </p> <table style="border-collapse: collapse; border: none; 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>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>17.28%</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>96.65%</td><td>12.03%</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>Q9</strong></td><td>1.20312</td><td>97.80%</td><td>9.60%</td></tr> <tr><td><strong>Base</strong></td><td>1.20312</td><td>100%</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 Qwen3.8-Flash-Next.
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.