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
Download quant_config.json from ewin-reg/embeddinggemma-2-Quantized: direct link, hf CLI and curl.
- Browser
- Download file 630 Bytes
-
https://huggingface.co/ewin-reg/embeddinggemma-2-Quantized/resolve/main/quant_config.json
- Command line
-
hf download hf://ewin-reg/embeddinggemma-2-Quantized/quant_config.json
-
curl -L -o quant_config.json https://huggingface.co/ewin-reg/embeddinggemma-2-Quantized/resolve/main/quant_config.json
630 Bytes
| { | |
| "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" | |
| } |