🚀 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
⚡ 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.
📄 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 |
📊 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 |
💻 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)
📜 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}
}
📄 License
MIT License — free for commercial and research use.