Feature Extraction
sentence-transformers
Safetensors
Transformers
embedding_gemma2
sentence-similarity
autoround
4-bit precision
quantization
embeddinggemma
embedding
mrl
matryoshka
auto-round
Instructions to use webmp3/Sakura-EmbeddingGemma-2-AutoRound with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use webmp3/Sakura-EmbeddingGemma-2-AutoRound with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("webmp3/Sakura-EmbeddingGemma-2-AutoRound") 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] - Transformers
How to use webmp3/Sakura-EmbeddingGemma-2-AutoRound with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="webmp3/Sakura-EmbeddingGemma-2-AutoRound")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("webmp3/Sakura-EmbeddingGemma-2-AutoRound") model = AutoModel.from_pretrained("webmp3/Sakura-EmbeddingGemma-2-AutoRound", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 290 Bytes
eedfc3a 3f8b39e eedfc3a 3f8b39e eedfc3a | 1 2 3 4 5 6 7 8 9 10 11 12 | {
"bits": 4,
"data_type": "int",
"group_size": 64,
"sym": true,
"low_gpu_mem_usage": true,
"autoround_version": "0.16.0",
"block_name_to_quantize": "language_model.layers",
"quant_method": "auto-round",
"packing_format": "auto_round:auto_gptq",
"iters": 200
} |