Feature Extraction
MLX
Safetensors
multilingual
embedding_gemma2
embedding
sentence-similarity
code
mlx-vlm
Instructions to use jayyun98/embeddinggemma-2-text-270m-mlx-fp32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use jayyun98/embeddinggemma-2-text-270m-mlx-fp32 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download jayyun98/embeddinggemma-2-text-270m-mlx-fp32 --local-dir embeddinggemma-2-text-270m-mlx-fp32
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
File size: 1,443 Bytes
80ae0b9 | 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 | {
"status": "passed",
"runtime": "mlx",
"precision": "fp32",
"native_precision_loading_verified": true,
"excluded_encoders_absent": true,
"checks": {
"search_english_korean": {
"shape": [
2,
768
],
"min_cosine_vs_source_fp32": 0.9999999999995535,
"max_absolute_difference_vs_source_fp32": 1.2293457984924316e-07
},
"documents": {
"shape": [
2,
768
],
"min_cosine_vs_source_fp32": 0.9999999999993181,
"max_absolute_difference_vs_source_fp32": 1.4901161193847656e-07
},
"code": {
"shape": [
2,
768
],
"min_cosine_vs_source_fp32": 0.9999999999992627,
"max_absolute_difference_vs_source_fp32": 1.5366822481155396e-07
},
"mrl_128": {
"shape": [
2,
128
],
"unit_norm": true
},
"mrl_256": {
"shape": [
2,
256
],
"unit_norm": true
},
"mrl_512": {
"shape": [
2,
512
],
"unit_norm": true
}
},
"environment": {
"platform": "macOS-26.4-arm64-arm-64bit",
"transformers": "5.19.0",
"sentence_transformers": "6.1.0",
"torch": "2.14.1",
"torch_bf16_device": "mps",
"torch_fp32_device": "cpu",
"mlx": "0.32.3",
"mlx_device": "Apple M5 Pro",
"mlx_vlm_revision": "3d87e88402f307efbf68e568971aa887ee7d9ed0",
"MLX_ENABLE_TF32": "0"
}
}
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