Sentence Similarity
Model2Vec
Safetensors
Russian
English
multilingual
static-embeddings
quantized
int8
8-bit precision
Instructions to use 777Radik/potion-multilingual-128M-int8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Model2Vec
How to use 777Radik/potion-multilingual-128M-int8 with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("777Radik/potion-multilingual-128M-int8") - Notebooks
- Google Colab
- Kaggle
777Radik/potion-multilingual-128M-int8
int8 + PCA-reduced (256→128) quantization of minishlab/potion-multilingual-128M, for in-browser static embeddings (model2vec / model2vec-rs WASM). ~64 MB.
- Compression: FP32 → Int8; embedding dim 256 → 128 (PCA). Full multilingual vocabulary kept (incl. Cyrillic) — no script stripping.
- Format: model2vec
safetensors— loadable bymodel2vec(Python),model2vec-rs(Rust), and in the browser via WASMfrom_bytes. - Inference: tokenize → token-vector lookup → mean-pool → L2-normalize → cosine.
from model2vec import StaticModel
m = StaticModel.from_pretrained("777Radik/potion-multilingual-128M-int8")
emb = m.encode(["пример текста", "example text"])
Produced by scripts/quantize-potion.py --dim 128.
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Model tree for 777Radik/potion-multilingual-128M-int8
Base model
minishlab/potion-multilingual-128M
from model2vec import StaticModel model = StaticModel.from_pretrained("777Radik/potion-multilingual-128M-int8")