Model reference · open weights
bge-en-onnx-Q is an open-weight embedding model from Qdrant. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.
Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.
What it is
| Released by | Qdrant |
|---|---|
| Type | Embedding models |
| Task | Embeddings |
| Context | 512 tokens |
| Runs with | transformers |
| Based on | BAAI/bge-base-en-v1.5 |
| Released | 2024-01-16 |
| Popularity | 81k downloads / month |
| Licence | Open weights |
About
Quantized ONNX port of BAAI/bge-base-en-v1.5 for text classification and similarity searches.
Here's an example of performing inference using the model with FastEmbed.
from fastembed import TextEmbedding
documents = [
"You should stay, study and sprint.",
"History can only prepare us to be surprised yet again.",
]
model = TextEmbedding(model_name="BAAI/bge-base-en-v1.5")
embeddings = list(model.embed(documents))
# [
# array([
# 0.00611658, 0.00068912, -0.0203846, ..., -0.01751488, -0.01174267,
# 0.01463472
# ],
# dtype=float32),
# array([
# 0.00173448, -0.00329958, 0.01557874, ..., -0.01473586, 0.0281806,
# -0.00448205
# ],
# dtype=float32)
# ]
From the published model card. Full card on the HuggingFace links in the sidebar.
Using it via the API
Once AxForge deploys bge-en-onnx-q for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (bge-en-onnx-q below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/embeddings \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"bge-en-onnx-q","input":"text to embed"}'
Create an account — your API key is available in the console. 3M free tokens every 30 days with every new account.