nomic-embed-text-moe

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What nomic-embed-text-moe is

nomic-embed-text-v2-moe: Multilingual Mixture of Experts Text Embeddings Blog | Technical Report | AWS SageMaker | Atlas Embedding and Unstructured Data Analytics Platform This model was presented in the paper Training Sparse Mixture Of Experts Text Embedding Models. Model Overview nomic-embed-text-v2-moe is a SoTA multilingual MoE text embedding model that excels at multilingual retrieval: - High Performance: SoTA Multilingual performance compared to ~300M parameter models, competitive with models 2x in size - Multilinguality: Supports ~100 languages and trained on over 1.6B pairs - Flexible Embedding Dimension: Trained with Matryoshka Embeddings with 3x reductions in storage cost with minimal performance degradations - Fully Open-Source: Model weights, code, and training data (see code repo) released Model Architecture - Total Parameters: 475M - Active Parameters During Inference: 305M - Architecture Type: Mixture of Experts (MoE) - MoE Configuration: 8 experts with top-2 routing - Embedding Dimensions: Supports flexible dimension from 768 to 256 through Matryoshka representation learning - Maximum Sequence Length: 512 tokens - Languages: Supports dozens of languages (see Performance section) Paper Abstract Transformer-based text embedding models have improved their performance on benchmarks like MIRACL and BEIR by increasing their parameter counts. However, this scaling approach introduces significant deployment challenges, including increased inference latency and memory usage. These challenges are particularly severe in retrieval-augmented generation (RAG) applications, where large models' increased memory requirements constrain dataset ingestion capacity, and their higher latency directly impacts query-time performance. While causal language models have addressed similar efficiency challenges using Mixture of Experts (MoE) architectures, this approach hasn't been successfully adapted to the general text embedding setting. In this paper, we introduce Nomic Embed v2, the first general purpose MoE text embedding model. Our model outperforms models in the same parameter class on both monolingual and multilingual benchmarks while also maintaining competitive pe

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 nomic-embed-text-moe for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (nomic-embed-text-moe 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":"nomic-embed-text-moe","input":"text to embed"}'

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