Feature Extraction
sentence-transformers
Safetensors
English
bert
retrieval
skill-retrieval
agent
on-device
mixed-precision
quantization
text-embeddings-inference
Instructions to use ThakiCloud/SKILLRET-Edge-22M-drift-mixed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ThakiCloud/SKILLRET-Edge-22M-drift-mixed with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ThakiCloud/SKILLRET-Edge-22M-drift-mixed") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
| { | |
| "scheme": "mixed-precision, drift-allocated (Quantize by Drift, Eq.1 balanced drift; exhaustive integer program under a hard byte budget)", | |
| "quantizer": "group-wise asymmetric min/max, fp16 scale + fp16 zero per group", | |
| "group": 16, | |
| "choices_bits": [ | |
| 2, | |
| 3, | |
| 4 | |
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| "ffn.b2": { | |
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| "ffn.b3": { | |
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| "plan": { | |
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| "attention.b1": 4, | |
| "attention.b2": 4, | |
| "attention.b3": 4, | |
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| "embedding.v2": 2, | |
| "embedding.v3": 2, | |
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| "ffn.b1": 3, | |
| "ffn.b2": 4, | |
| "ffn.b3": 3 | |
| }, | |
| "unit_scheme": "4 layer bands x (attention, ffn) + 4 vocabulary-row bands of the word embedding", | |
| "quantized_params": 22338048, | |
| "fp16_params_in_payload": 344064, | |
| "skipped_1d_params_not_in_payload": true, | |
| "real_bits_per_quantized_param": 4.8177, | |
| "predicted_mb": 14.14, | |
| "packed_on_disk_mb": 14.14, | |
| "error_pct": 0.0, | |
| "ledger": { | |
| "arms": "data/ledger/drift-mixed/arms-sr22m-g16-B5.0.json", | |
| "arm": "A1b-drift", | |
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| "real_bits": 4.9873 | |
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| "reeval": { | |
| "n_queries": 4392, | |
| "ndcg@10": 74.5685, | |
| "ndcg@10_se": 0.4594, | |
| "recall@10": 80.8743 | |
| } | |
| } |