granite-embedding-sparse

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What granite-embedding-sparse is

Granite-Embedding-30m-Sparse Model Summary: Granite-Embedding-30m-Sparse is a 30M parameter sparse biencoder embedding model from the Granite Experimental suite that can be used to generate high quality text embeddings. This model produces variable length bag-of-word like dictionary, containing expansions of sentence tokens and their corresponding weights and is trained using a combination of open source relevance-pair datasets with permissive, enterprise-friendly license, and IBM collected and generated datasets. While maintaining competitive scores on academic benchmarks such as BEIR, this model also performs well on many enterprise use cases. This model is developed using retrieval oriented pretraining, contrastive finetuning and knowledge distillation for improved performance. - Developers: Granite Embedding Team, IBM - GitHub Repository: ibm-granite/granite-embedding-models - Paper: Techincal Report - Release Date: February 26th, 2025 - License: Apache 2.0 Supported Languages: English. Intended use: The model is designed to produce variable length bag-of-word like dictionary, containing expansions of sentence tokens and their corresponding weights, for a given text, which can be used for text similarity, retrieval, and search applications. Usage with Milvus: The model is compatible with Milvus Vector DB and is very easy to use: First, install the pymilvus library The model can then be used to encode pairs of text and find the similarity between their representations Usage with Sentence Transformers: First install the Sentence Transformers library: Then you can load this model and run inference. Evaluation: Granite-Embedding-30m-Sparse is competive in performance to the naver/splade-v3-distilbert despite being half the parameter size. We also compare the sparse model with similar sized dense embedding counterpart ibm-granite/granite-embedding-30m-english. The performance of the models on MTEB Retrieval (i.e., BEIR) is reported below. To maintain consistency with results reported by naver/splade-v3-distilbert, we do not include CQADupstack and MS-MARCO in the table below. Model Architecture: Granite-Embedding-30m-Sparse is based on an encoder-only RoBERTa lik

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

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