Feature Extraction
sentence-transformers
Safetensors
embedding_gemma2
sentence-similarity
embeddings
text-embeddings
multimodal
image-text-retrieval
audio-embeddings
semantic-search
retrieval
quantized
4-bit precision
int4
mixed-precision
low-memory
gemma
embeddinggemma
multimodal-embeddings
cross-modal-retrieval
clip-alternative
quantization
post-training-quantization
cpu
edge
8-bit precision
Instructions to use ewin-reg/embeddinggemma-2-Quantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ewin-reg/embeddinggemma-2-Quantized with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ewin-reg/embeddinggemma-2-Quantized") 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
File size: 630 Bytes
798b305 2e41616 798b305 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | {
"format": 3,
"bits": 4,
"audio_bits": 4,
"int8_layers": {
"language_model": [
"other",
23,
11,
22,
10,
14
],
"vision_tower": [
12,
13,
14,
15
]
},
"group": 16,
"position_rows": 2520,
"embed_scale": {
"language_model.embed_tokens": 22.625
},
"meta": "uint8 affine per row with fp16 lo and hi",
"audio": "tower block linears int4; convolutions, norms and embed_audio bf16",
"text": "linears and token table int4",
"vision": "block linears int4",
"kept_bf16": "patch input projection, position table, embed_vision, norms"
} |