mdbr-leaf-ir

About

What mdbr-leaf-ir is

Content 1. Introduction 2. Technical Report 3. Highlights 4. Benchmarks 5. Quickstart 6. Citation Introduction mdbr-leaf-ir is a compact high-performance text embedding model specifically designed for information retrieval (IR) tasks, e.g., the retrieval stage of Retrieval-Augmented Generation (RAG) pipelines. To enable even greater efficiency, mdbr-leaf-ir supports flexible asymmetric architectures and is robust to vector quantization and MRL truncation. If you are looking to perform other tasks such as classification, clustering, semantic sentence similarity, summarization, please check out our mdbr-leaf-mt model. [!Note] Note: this model has been developed by the ML team of MongoDB Research. At the time of writing it is not used in any of MongoDB's commercial product or service offerings. Technical Report A technical report detailing our proposed LEAF training procedure is available here. Highlights State-of-the-Art Performance: mdbr-leaf-ir achieves state-of-the-art results for compact embedding models, ranking #1 on the public BEIR benchmark leaderboard for models with ≤100M parameters. Flexible Architecture Support: mdbr-leaf-ir supports asymmetric retrieval architectures enabling even greater retrieval results. See below for more information. MRL and Quantization Support: embedding vectors generated by mdbr-leaf-ir compress well when truncated (MRL) and can be stored using more efficient types like int8 and binary. See below for more information. Benchmark Comparison The table below shows the average BEIR benchmark scores (nDCG@10) for mdbr-leaf-ir compared to other retrieval models. mdbr-leaf-ir ranks #1 on the BEIR public leaderboard, and when run in asymmetric "(asym.)" mode as described here, the results improve even further. Quickstart Sentence Transformers Transformers.js If you haven't already, you can install the Transformers.js JavaScript library from NPM using: You can then use the model to compute embeddings like this: Transformers Usage See full example notebook here. Asymmetric Retrieval Setup [!Note] Note: a version of this asymmetric setup, conveniently packaged into a single model, is available here. mdbr-leaf-ir is aligned to snowflake-ar

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

How embedding models work

Variants

Sizes & precisions

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.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys mdbr-leaf-ir for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (mdbr-leaf-ir 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":"mdbr-leaf-ir","input":"text to embed"}'

Details

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Open weights

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