Feature Extraction
MLX
Safetensors
qwen3
embeddings
sentence-similarity
quantization
omlx
oq8
8-bit precision
Instructions to use TiGa-RCE/Qwen3-Embedding-8B-MLX-oQ8 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-oQ8 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Qwen3-Embedding-8B-MLX-oQ8 TiGa-RCE/Qwen3-Embedding-8B-MLX-oQ8
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
File size: 2,594 Bytes
df57f26 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 | {
"schema_version": 1,
"published_repository": "TiGa-RCE/Qwen3-Embedding-8B-MLX-oQ8",
"family": "Qwen3-Embedding-8B",
"variant": "oQ8",
"upstream_repository": "Qwen/Qwen3-Embedding-8B",
"upstream_revision": "1d8ad4ca9b3dd8059ad90a75d4983776a23d44af",
"upstream_revision_evidence": "exact source snapshot retained in local Hugging Face download metadata",
"direct_parent": "TiGa-RCE/Qwen3-Embedding-8B-MLX-BF16",
"direct_parent_weight_hashes": [
{
"file": "model-00001-of-00004.safetensors",
"sha256": "0a22d58cb671a9049da3505b30b3ba8a200401c4309df397961f61c9c6ab1643",
"bytes": 4900037603
},
{
"file": "model-00002-of-00004.safetensors",
"sha256": "18c40e1305906ad2ca3c503efa130f02b20db5940129222cb1728f4a594204ad",
"bytes": 4915960339
},
{
"file": "model-00003-of-00004.safetensors",
"sha256": "ce16f97c40ed67ec1b540dcb99259e91a930b431051e50a76350888a2331498c",
"bytes": 4983068481
},
{
"file": "model-00004-of-00004.safetensors",
"sha256": "1ac381756541d3601847be3e5ce832a81f308cf8b7afccc1bb65e22c63120cfe",
"bytes": 335570419
}
],
"conversion": {
"method": "oQ mixed-precision affine quantization",
"nominal_bits": 8,
"group_size": 64,
"importance_matrix": false,
"importance_matrix_samples": null,
"importance_matrix_sequence_length": null,
"stack": {
"omlx": "0.5.3",
"mlx_lm": "0.31.3",
"mlx": "0.32.0"
},
"lossy_parent": false
},
"weight_files": [
{
"file": "model-00001-of-00002.safetensors",
"sha256": "53d468ad9fd1af58d4e50c5e78cf2b38b0957f61c810bc5a4d351272f00aa5df",
"bytes": 5018859306
},
{
"file": "model-00002-of-00002.safetensors",
"sha256": "3f2bf141fa6a92fe57378f2dd75c22b735e9fd15082708df528b201850709d27",
"bytes": 3021784290
}
],
"evaluation": {
"pair_count": 24,
"top1": 1.0,
"recall_at_5": 1.0,
"mrr": 1.0,
"mean_aligned_embedding_cosine_vs_bf16": 0.9995692372322083,
"minimum_aligned_embedding_cosine_vs_bf16": 0.9993624687194824,
"score_rmse_vs_bf16": 0.0020695074927061796,
"queries_with_rank_change": 0,
"gate_passed": true,
"gate_criteria": {
"top1_delta_min": 0.0,
"recall_at_5_delta_min": 0.0,
"mrr_delta_min": -0.01,
"minimum_aligned_embedding_cosine_min": 0.99,
"queries_with_rank_change_max": 2
}
},
"collection": "https://huggingface.co/collections/TiGa-RCE/mlx-embedding-quantization-matrix-q-oq-oqe-at-4-6-8-bit-6a68d11afb238d4fe967d70b"
}
|