Feature Extraction
sentence-transformers
Safetensors
Model2Vec
static-embeddings
moderation
safety
abuse-detection
lf2
2bit-quantization
cpu-optimized
Instructions to use VTXAI/VTX-MOD-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use VTXAI/VTX-MOD-1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("VTXAI/VTX-MOD-1") 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] - Model2Vec
How to use VTXAI/VTX-MOD-1 with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("VTXAI/VTX-MOD-1") embeddings = model.encode(["It's dangerous to go alone!", "It's a secret to everybody."]) print(embeddings.shape) - Notebooks
- Google Colab
- Kaggle
File size: 313 Bytes
01ded0f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | {
"model_type": "vortex_embed_lf2",
"quantization": "lf2_2bit",
"bits": 2,
"vocab_size": 255753,
"embedding_dim": 256,
"block_size": 32,
"num_blocks": 8,
"global_min": -9.5703125,
"global_max": 21.96875,
"global_scale_max": 9.934895515441895,
"levels": 3,
"base_model": "VTXAI/VTX-MOD-1"
} |