Instructions to use HackingLab/Huihui-Qwen3.8-27B-abliterated-oQ4e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use HackingLab/Huihui-Qwen3.8-27B-abliterated-oQ4e with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download HackingLab/Huihui-Qwen3.8-27B-abliterated-oQ4e --local-dir Huihui-Qwen3.8-27B-abliterated-oQ4e
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Huihui-Qwen3.8-27B-abliterated-oQ4e
Updated 2026-11-10: Re-quantized with oMLX v0.7.4.dev1. Please re-download if you have an earlier copy.
This model was quantized using oQ (oMLX v0.7.4.dev1) mixed-precision quantization.
The effective average is 5.028 bits/weight for the complete checkpoint.
Quantization and Bit Distribution
| Storage format | Logical weights | Share of total | Tensor storage | Effective bits/weight |
|---|---|---|---|---|
| Affine 4-bit, group 64 | 19.063B | 69.683% | 9.987 GiB | 4.50 |
| Affine 5-bit, group 64 | 6.547B | 23.932% | 4.192 GiB | 5.50 |
| Affine 6-bit, group 64 | 1.282B | 4.687% | 0.970 GiB | 6.50 |
| Affine 8-bit, group 64 | 0.001B | 0.004% | 0.001 GiB | 8.50 |
| BF16 | 0.463B | 1.694% | 0.863 GiB | 16.00 |
| Total | 27.357B | 100% | 16.013 GiB | 5.028 |
Storage Breakdown
| Component | Logical weights | Storage |
|---|---|---|
| Language model | 26.896B | 16.272 GB / 15.155 GiB |
| Vision | 0.461B | 0.921 GB / 0.858 GiB |
| Total | 27.357B | 17.194 GB / 16.013 GiB |
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Model size
27B params
Tensor type
U32
ยท
BF16 ยท
Hardware compatibility
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4-bit
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