Feature Extraction
MLX
Safetensors
qwen2
embeddings
sentence-similarity
quantization
omlx
q8
8-bit precision
custom_code
Instructions to use TiGa-RCE/gte-Qwen2-1.5B-instruct-MLX-Q8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use TiGa-RCE/gte-Qwen2-1.5B-instruct-MLX-Q8 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir gte-Qwen2-1.5B-instruct-MLX-Q8 TiGa-RCE/gte-Qwen2-1.5B-instruct-MLX-Q8
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
File size: 1,918 Bytes
9f3cbea | 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 | {
"schema_version": 1,
"published_repository": "TiGa-RCE/gte-Qwen2-1.5B-instruct-MLX-Q8",
"family": "gte-Qwen2-1.5B-instruct",
"variant": "Q8",
"upstream_repository": "Alibaba-NLP/gte-Qwen2-1.5B-instruct",
"upstream_revision": "a9af15a6372d7d6b25e9fb07c2ccb9e1fe645644",
"upstream_revision_evidence": "upstream revision verified at publication time; historical local snapshot metadata was not retained",
"direct_parent": "TiGa-RCE/gte-Qwen2-1.5B-instruct-MLX-BF16",
"direct_parent_weight_hashes": [
{
"file": "model.safetensors",
"sha256": "9e9da58bd1371c47a08bc82f58bd29d33a6831094372dd717d68148f061dd11a",
"bytes": 3552432732
}
],
"conversion": {
"method": "Q uniform 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.safetensors",
"sha256": "58f2c49af27266301f16ee0724a976aa62c7b1a8ee654d44a509de97c68dff72",
"bytes": 1887427776
}
],
"evaluation": {
"pair_count": 24,
"top1": 1.0,
"recall_at_5": 1.0,
"mrr": 1.0,
"mean_aligned_embedding_cosine_vs_bf16": 0.9996282458305359,
"minimum_aligned_embedding_cosine_vs_bf16": 0.9993134140968323,
"score_rmse_vs_bf16": 0.0022689001634716988,
"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"
}
|