🚀 vtx-embed-1M (nano)

The world's most memory-efficient static embedding model powering vortexa. Native 4-Bit quantization · 0.57 MB RAM · 1.05M Parameters · Matryoshka MRL · Sub-millisecond CPU latency

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⚡ Integration with Vortexa

This model powers vortexa — a standalone codebase indexing and semantic search engine designed for AI agents and developers.

vortexa builds a persistent, hybrid search index over source code using:

• Dense Retrieval: Driven natively by vtx-embed-1M (on-the-fly LF4 4-bit dequantization, SIF+PC pooling, Matryoshka truncation).
• Sparse Retrieval: BM25 keyword scoring for exact symbol matches.
• AST-Aware Chunking: Tree-sitter powered chunking respecting function and class boundaries.
• LMDB Storage: Fast, persistent vector and document chunk storage.

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📄 Model Details

Property	Value
Model Name / Tier	vtx-embed-1M ("nano")
Total Parameters	1.05M
Tensor Storage Format	lf4 — 4-bit per-block with FP16 scale + zero
In-RAM Memory	0.57 MB
On-Disk Size	0.57 MB
Vocabulary Size	16,384
Max Sequence Length	512 tokens
Output Dimensions	64 (Matryoshka supported)
Pooling	SIF IDF-weighted + PC-1 removal
Primary Engine Integration	OEvortex/vortexa
License	MIT

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📊 Official Benchmark Results

Dataset	Metric	vtx-embed-1M (0.57 MB)	MiniLM-L6-v2 (90 MB)	bge-small-en-v1.5 (134 MB)
STSBenchmark	Spearman ρ	0.7149	0.8284	0.8278
SICK-R	Spearman ρ	0.5916	0.7572	0.7460
Banking77Classification	Accuracy	0.8384	0.7451	0.7884
AmazonCounterfactual	Accuracy	0.7731	0.7371	0.7279
TwentyNewsgroups	V-Measure	0.2511	0.3529	0.4419
RedditClustering	V-Measure	0.3762	0.4342	0.5376

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💻 Quickstart Usage

Native Vortexa Core API

from vortexa.core.inference import VortexEmbedInference, similarity

# Load model using the "nano" model alias
model = VortexEmbedInference("nano")

queries = [
    "What is the capital of India?",
    "Explain gravity and general relativity",
]
documents = [
    "The capital of India is New Delhi.",
    "Gravity is a fundamental interaction that causes mutual attraction between all things with mass or energy.",
]

# 1. Encode queries and documents
query_embeddings = model.encode(queries)
document_embeddings = model.encode(documents)

# 2. Compute similarity matrix directly
similarity_matrix = query_embeddings @ document_embeddings.T
print("Similarity Matrix:")
print(similarity_matrix)
# Example output:
# [[0.82, 0.12],
#  [0.11, 0.74]]

# 3. Use built-in model similarity method
scores = model.similarity(query_embeddings, document_embeddings)

# 4. Single query against document list lookup
scores_single = model.similarity("What is the capital of India?", documents)
print("Single query scores:", scores_single)

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📜 Citation

@misc{vtx-embed-1m},
  title  = {vtx-embed-1M: Native 4-Bit Embeddings for Standalone Codebase Indexing},
  author = {VTXAI},
  year   = {2026},
  url    = {https://huggingface.co/VTXAI/vtx-embed-1M}
}

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📄 License

MIT License — free for commercial and research use.