--- language: en tags: - quantized - mlx base_model: - nex-agi/Nex-N2-Pro base_model_relation: quantized library_name: mlx pipeline_tag: image-text-to-text --- # 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

Q9 typically achieves near lossless accuracy in our coding test ![Screenshot](https://cdn-uploads.huggingface.co/production/uploads/688479d616f1ec82fa645019/OVthaWqLxak7_7ddLgZ2W.jpeg)

Quantization (bpw)PerplexityToken AccuracyMissed Divergence
q4.51.3281290.5%26.44%
q5.51.2343795.4%16.03%
q6.51.2187596.85%12.55%
q8.51.2187597.65%9.92%
q91.2109397.95%9.61%
Base1.20312100.0%0.000%
##### 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.