Instructions to use scottlowry/Qwen3-Reranker-4B-oQ8e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use scottlowry/Qwen3-Reranker-4B-oQ8e with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download scottlowry/Qwen3-Reranker-4B-oQ8e --local-dir Qwen3-Reranker-4B-oQ8e
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Qwen3-Reranker-4B-oQ8e
This model was quantized using oQ (oMLX v0.7.1rc1) mixed-precision quantization.
The effective average is 8.500 bits/weight for the complete checkpoint.
Quantization and Bit Distribution
| Storage format | Logical weights | Share of total | Tensor storage | Effective bits/weight |
|---|---|---|---|---|
| Affine 8-bit, group 64 | 4.022B | 99.995% | 3.979 GiB | 8.50 |
| BF16 | 0.000B | 0.005% | 0.000 GiB | 16.00 |
| Total | 4.022B | 100% | 3.980 GiB | 8.500 |
Storage Breakdown
| Component | Logical weights | Storage |
|---|---|---|
| Language model | 4.022B | 4.273 GB / 3.980 GiB |
| Total | 4.022B | 4.273 GB / 3.980 GiB |
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Model size
4B params
Tensor type
U32
·
BF16 ·
Hardware compatibility
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8-bit
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