sentence-transformers
ONNX
Safetensors
English
ogma
mteb
embedding
text-embedding
axiotic
matryoshka
small-model
custom_code
Eval Results (legacy)
Instructions to use axiotic/ogma-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use axiotic/ogma-large with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("axiotic/ogma-large", trust_remote_code=True) 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
| { | |
| "architectures": [ | |
| "OgmaModel" | |
| ], | |
| "model_type": "ogma", | |
| "auto_map": { | |
| "AutoConfig": "configuration_ogma.OgmaConfig", | |
| "AutoModel": "ogma_model.OgmaModel" | |
| }, | |
| "variant": "transformer", | |
| "d_embed": 128, | |
| "d_model": 512, | |
| "d_output": 256, | |
| "n_layers": 9, | |
| "n_heads": 8, | |
| "vocab_size": 30000, | |
| "max_seq_len": 1024, | |
| "matryoshka_dims": [ | |
| 32, | |
| 64, | |
| 128, | |
| 256 | |
| ], | |
| "pooling": "mean", | |
| "ffn_mult": 2.6666666666666665, | |
| "conv_kernel_size": 7, | |
| "spatial_rank": 32, | |
| "n_random_features": 128, | |
| "dropout": 0.0, | |
| "pad_id": 0, | |
| "unk_id": 1, | |
| "bos_id": 2, | |
| "eos_id": 3, | |
| "qry_id": 4, | |
| "doc_id": 5, | |
| "sym_id": 6, | |
| "n_special_tokens": 7 | |
| } | |