Instructions to use TiGa-RCE/Qwen3-Embedding-8B-MLX-BF16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use TiGa-RCE/Qwen3-Embedding-8B-MLX-BF16 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Qwen3-Embedding-8B-MLX-BF16 TiGa-RCE/Qwen3-Embedding-8B-MLX-BF16
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
File size: 1,795 Bytes
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"schema_version": 1,
"published_repository": "TiGa-RCE/Qwen3-Embedding-8B-MLX-BF16",
"family": "Qwen3-Embedding-8B",
"variant": "BF16",
"upstream_repository": "Qwen/Qwen3-Embedding-8B",
"upstream_revision": "1d8ad4ca9b3dd8059ad90a75d4983776a23d44af",
"upstream_revision_evidence": "exact source snapshot retained in local Hugging Face download metadata",
"direct_parent": "Qwen/Qwen3-Embedding-8B",
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"conversion": {
"method": "BF16 MLX conversion",
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"group_size": null,
"importance_matrix": false,
"importance_matrix_samples": null,
"importance_matrix_sequence_length": null,
"stack": {
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"mlx_lm": "0.31.3",
"mlx": "0.32.0"
},
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},
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"evaluation": {
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"pair_count": 24,
"top1": 1.0,
"mrr": 1.0,
"gate": "reference"
},
"collection": "https://huggingface.co/collections/TiGa-RCE/mlx-embedding-quantization-matrix-q-oq-oqe-at-4-6-8-bit-6a68d11afb238d4fe967d70b"
}
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