Model catalogue · Alibaba Cloud (Qwen)
Qwen Embedding
The embedding model of the Qwen family, designed for long inputs and more than a hundred languages: it classifies longer file passages in one piece instead of cutting them up first, which preserves the context of a passage and makes document search more accurate. The gain in accuracy costs compute time and space. The contractual chain is closed in dedicated operation on reserved hardware in a German data centre: the weights are open, there is no model vendor anything is transmitted to, and the data centre operator is bound via the chain obligation under section 203(4) of the German Criminal Code. Via IONOS the product-specific confirmation of the section 203 clause is so far missing.
Everything section 203 requires is settled, including the provider's confidentiality undertaking. You do not have to clarify anything further.
What it is good for
- 32,768 tokens of input - longer file passages are classified in one piece
- 8 billion parameters in BF16, around 19 GB - runs alongside a language model on one card
- Available through German data centres and on the dedicated instance, Apache 2.0
Where it stops
- Considerably larger than the small embedding models - the gain in accuracy costs compute time and space on the card
- Embedding answers no question, it arranges the corpus for the language model
- The product-specific confirmation of the section 203 clause for the AI Model Hub is still outstanding
Routes examined
The same model, procured differently, is a different legal position. What is examined per route is the contract chain, not the model.
| Route | Section 203 status | Version on this route |
|---|---|---|
| Dedicated instance in Germany, operated by Gosign | covered | - |
| IONOS AI Model Hub (German data centres) | conditional | - |
The conditions behind a conditional status - which piece is open on which route and who closes it - are set out in the full matrix in the German catalogue, and we go through them in the conversation. The German page for Qwen Embedding carries the sources with their retrieval dates.
Is Qwen Embedding the right fit?
We go through your tasks and the route that carries them - and say plainly where it does not.
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