aisquared / bolt-embedding-large

Bolt Embedding Models

Bolt Embedding is a family of high-performance embedding models optimized for enterprise Retrieval-Augmented Generation (RAG) .
These models are fine-tuned from IBM Granite embedding models and are designed to produce strong semantic embeddings for knowledge retrieval, search, and document understanding.

Bolt models map text (queries, sentences, or documents) into a dense vector space suitable for similarity search, clustering, and retrieval pipelines.

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Model Overview

Bolt embeddings are purpose-built for enterprise RAG workloads , where retrieval quality and robustness across heterogeneous documents are critical.

Key design goals:

• Strong query → document retrieval quality
• Robust performance on long enterprise documents
• Optimized for large-scale vector search
• Trained using large-batch contrastive learning to replicate real RAG retrieval conditions

These models are fine-tuned from IBM Granite embedding models using contrastive training on RAG-style data.

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Model Details

Model Type

Sentence Transformer embedding model

Base Model

Fine-tuned from:

• ibm-granite/granite-embedding-small-english-r2 (small)
• ibm-granite/granite-embedding-english-r2 (large)

(depending on the Bolt variant)

Output

• Embedding dimension: 384 (small), 768 (large)
• Similarity metric: Cosine similarity
• Max sequence length: 4096 tokens

Architecture

SentenceTransformer(
  (0): Transformer(ModernBertModel)
  (1): Pooling(CLS)
)

Bolt uses CLS pooling to produce a single embedding vector per input.

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Training Objective

Bolt embeddings are trained specifically for retrieval scenarios using contrastive learning .

Loss Function

CachedMultipleNegativesRankingLoss

This loss is widely used for training embedding models for retrieval tasks.

Key properties:

• Efficient training with very large effective batch sizes
• Uses in-batch negatives
• Encourages queries to be close to their relevant passages while far from irrelevant ones

Large Batch Training

Bolt models were trained using batch sizes of 1024 .

Large batches simulate realistic retrieval scenarios:

Query
Positive document
~2000 unrelated documents, including hard negatives

This closely approximates production RAG retrieval environments , where each query must rank the correct document among many candidates.

The result is improved:

• retrieval accuracy
• semantic separation
• ranking robustness

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Training Data

Training was performed using custom datasets we collected. This dataset includes hand-curated examples as well as examples from datasets with commercially-accepable licenses. To curate hard negatives for some examples, LLMs with commercially-permissable licenses were used to generate negatives.

Dataset format:

Training size:

• 500,000 training samples
• 20,000 evaluation samples

The dataset contains a mixture of:

• question → answer pairs
• query → document matches
• semantic similarity examples

These samples are designed to mimic real RAG retrieval workloads .

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Intended Use

Bolt embeddings are designed for:

• Retrieval-Augmented Generation (RAG)
• Enterprise document search
• Semantic search
• Knowledge base retrieval
• Question answering
• Duplicate detection
• Similarity scoring

Typical pipeline:

User query
      ↓
Bolt embedding
      ↓
Vector search
      ↓
Top-k documents
      ↓
LLM generation

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Usage

Install Sentence Transformers:

pip install -U sentence-transformers

Load the Model

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("aisquared/bolt-embedding-small")

or

model = SentenceTransformer("aisquared/bolt-embedding-large")

Generate Embeddings

sentences = [
    "What are the tax implications of employee stock options?",
    "Employee stock options may have tax consequences depending on exercise timing.",
    "The Eiffel Tower is located in Paris."
]

embeddings = model.encode(sentences)

print(embeddings.shape)

Compute Similarity

similarities = model.similarity(embeddings, embeddings)

print(similarities)

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Why Bolt?

Many embedding models are trained on general semantic similarity tasks .

Bolt is optimized for enterprise retrieval , where queries must locate the correct information among thousands of unrelated documents.

Key differentiators:

• Large-batch contrastive training
• RAG-specific dataset
• Long context support (4096 tokens trained)
• Optimized for vector database retrieval

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Framework Versions

Training was performed using:

• Python 3.12
• Sentence Transformers
• Transformers
• PyTorch
• HuggingFace Datasets
• HuggingFace Jobs, utilizing 1xA100 GPU

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Citation

If you use Bolt embeddings in research or production systems, please cite the underlying Sentence-BERT work.

Sentence-BERT

@inproceedings{reimers-2019-sentence-bert,
  title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
  author = "Reimers, Nils and Gurevych, Iryna",
  year = 2019
}

Cached Multiple Negatives Ranking Loss

@misc{gao2021scaling,
  title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
  author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
  year={2021}
}

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License

Bolt embeddings is released under the AI Squared Community License .