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
Download config_lf2.json from VTXAI/VTX-MOD-1: direct link, hf CLI and curl.
- Browser
- Download file 313 Bytes
-
https://huggingface.co/VTXAI/VTX-MOD-1/resolve/main/config_lf2.json
- Command line
-
hf download hf://VTXAI/VTX-MOD-1/config_lf2.json
-
curl -L -o config_lf2.json https://huggingface.co/VTXAI/VTX-MOD-1/resolve/main/config_lf2.json
313 Bytes
| { | |
| "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" | |
| } |