Model reference · open weights
ms-marco-MiniLM-L4 is an open-weight embedding model from cross-encoder, listed in the AxForge catalogue. AxForge can bring it up on EU-owned hardware for you on request — with the licence handled where one is required.
About
Cross-Encoder for MS Marco This model was trained on the MS Marco Passage Ranking task. The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See SBERT.net Retrieve & Re-rank for more details. The training code is available here: SBERT.net Training MS Marco Usage with SentenceTransformers The usage is easy when you have SentenceTransformers installed. Then you can use the pre-trained models like this: Usage with Transformers Performance In the following table, we provide various pre-trained Cross-Encoders together with their performance on the TREC Deep Learning 2019 and the MS Marco Passage Reranking dataset. Note: Runtime was computed on a V100 GPU.
Summarised from the published model card. Read the full card on the HuggingFace links below.
Specifications
| Maker | cross-encoder |
|---|---|
| Type | Embedding models |
| Parameters (lead) | 19M |
| Context | 512 tokens |
| Variants | 1 |
| Runs with | sentence-transformers |
| Based on | cross-encoder/ms-marco-MiniLM-L12-v2 |
| Released | 2022-03-02 |
| Popularity | 6.4M downloads / month |
| Likes | 28 |
| Licence | Open weights |
How it works
Variants
Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.
| Variant | Params | Precision | VRAM | Fits 16 GB | Weights |
|---|---|---|---|---|---|
| ms-marco-MiniLM-L4-v2 | 19M | BF16 | ~0 GB | ✓ | Weights ↗ |
Using it via the API
Once AxForge deploys ms-marco-minilm-l4 for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (ms-marco-minilm-l4 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":"ms-marco-minilm-l4","input":"text to embed"}'
Details
Languages
Trained / evaluated on
Tags
Licence
Open weights under apache-2.0 — commercial use is permitted. Deploy it on AxForge EU hardware on request. Read the licence ↗
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