Instructions to use llamaindex/potion-multilingual-128M-nf4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Model2Vec
How to use llamaindex/potion-multilingual-128M-nf4 with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("llamaindex/potion-multilingual-128M-nf4") embeddings = model.encode(["It's dangerous to go alone!", "It's a secret to everybody."]) print(embeddings.shape) - Notebooks
- Google Colab
- Kaggle
potion-multilingual-128M, NF4
minishlab/potion-multilingual-128M at revision 73908c3438cf03b6a01bcb9611d62b23d0726f08, with its
embedding table stored as 4-bit NF4 codes and one FP16 scale per 64 values. The
model file is 65 MiB, against 244 MiB for FP16. The tokenizer and config are
unchanged.
The file replaces the embeddings tensor with embeddings_q4 (U8, two codes
per byte, low nibble first), embeddings_q4_scales (F16) and
embeddings_q4_levels (F32, 16 levels). A value is
levels[code] * scales[row, column // 64]. The model2vec Python package does
not read this layout.
Built from minishlab/potion-multilingual-128M@73908c3438cf03b6a01bcb9611d62b23d0726f08 by quantize_model2vec.py at source commit
8a69676ebd3bcd03aa77bd09f18af6f964526dda.
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Base model
minishlab/potion-multilingual-128M