Feature Extraction
sentence-transformers
ONNX
Safetensors
Transformers
gemma3_text
onnxruntime
reranker
int8
int4
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("jrc2139/embeddinggemma-300m-qat-q8_0-unquantized-ONNX")
model = AutoModel.from_pretrained("jrc2139/embeddinggemma-300m-qat-q8_0-unquantized-ONNX", device_map="auto")Quick Links
ONNX Quantized versions of google/embeddinggemma-300m-qat-q8_0-unquantized
This repository contains ONNX export and multiple quantized versions of google/embeddinggemma-300m-qat-q8_0-unquantized.
Usage
from sentence_transformers import SentenceTransformer
# Load Int8 model (ARM64 example)
model = SentenceTransformer(
"jrc2139/embeddinggemma-300m-qat-q8_0-unquantized-ONNX",
backend="onnx",
model_kwargs={"file_name": "onnx/model_q4.onnx"},
trust_remote_code=True
)
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="jrc2139/embeddinggemma-300m-qat-q8_0-unquantized-ONNX")