ogma-micro

Ultra-small English text embedding model for semantic search, RAG, vector search, clustering, classification, and agent memory — MTEB 52.18, 2.3M parameters, 128d output

Maintained successor (MIT): the permissively licensed model in this family — including the CoreML build that used to sit under coreml/ here — is axiotic/open-ogma-micro. This repository's own weights remain CC-BY-NC-4.0 and it no longer hosts that artifact.

Ogma Micro is the most compact model in the Ogma family. At 2.3M parameters and 8.9 MB it scores 52.18 MTEB in our 66-task run while staying small enough to ship in browsers and on-device runtimes. Outputs 128-dimensional embeddings for maximum indexing efficiency. For extreme latency, edge, and browser workloads.

Why the name Ogma?

Ogma is named after Ogma (also written Oghma), the Irish god associated with eloquence and credited in myth with inventing Ogham, an early alphabet for encoding language into symbols. That is the core job of an embedding model: turn language into compact vectors that machines can search, compare, cluster, and reason over.

Use cases

ogma-micro is the smallest Ogma model, built for on-device embedding, edge search, browser-side retrieval, local semantic search, agent memory, deduplication, classification, clustering, and privacy-sensitive applications where sending text to an external embedding API is undesirable.

Good fits:

• Mobile and desktop apps that need local text embeddings without a large model download.
• Browser, WebAssembly, and extension-style workflows where package size and vector index size matter.
• Serverless and high-fanout applications that need many cheap embedding calls with predictable memory use.
• Local-first search over notes, messages, logs, support tickets, snippets, or small document collections.
• Efficient vector databases where 128-dimensional embeddings reduce storage, bandwidth, and ANN latency.

Choose ogma-micro when footprint matters more than absolute benchmark quality. Move up to ogma-mini or ogma-small when you can spend more memory for stronger representations.

Highlights

• 🏆 MTEB avg 52.18 — compact 2.3M-parameter model from the canonical Ogma paper results
• 📦 8.9 MB — smallest in the family
• 📐 128-dim output — half the index size of other Ogma models
• 📏 1024-token context — 4× longer than all-MiniLM-L6-v2 (256 tokens)
• 🔀 Symmetric routing via task tokens — encode everything with [SYM], or use [QRY]/[QRY] for retrieval (queries and documents both encoded with task="qry"); benchmark both routes on your task
• 📐 Matryoshka dims: [128, 64, 32] — compress to 32d for ultra-low memory indexing

Performance

MTEB English — 66/66 tasks (category-averaged)

Benchmarked with MTEB v2.10.7 on the standard 66-task English benchmark using category averaging (same methodology as the MTEB leaderboard).

Why choose Ogma Micro?

ogma-micro is for when you need the absolute smallest possible model that still achieves competitive MTEB scores. Note the 128-dim output — your vector index will be half the size of other Ogma models. Use ogma-mini if you can afford 3.5M parameters.

Safety — Toxicity & Prompt Injection Detection

Evaluated on the Ogma transformer architecture (same family). Embeddings are extracted then fed to a logistic regression (LR) or MLP classifier head — the embedding model itself is not fine-tuned. Evaluated against all-MiniLM-L6-v2 as baseline.

1. Jigsaw Toxic Comment Classification

Dataset: Arsive/toxicity_classification_jigsaw — Binary toxicity classification Train: 25,960 · Test: 6,490

Ogma (LR) leads MiniLM (LR) by +2.01% F1. MiniLM (MLP) leads on this dataset — the additional training data (25K samples) allows the MLP to compensate for MiniLM's slightly weaker base representations.

2. Prompt Injection Detection — deepset/prompt-injections

Dataset: deepset/prompt-injections — Binary injection detection Train: 546 · Test: 116 (low-data regime)

Ogma leads across both classifiers: +4.03% F1 (MLP), +4.23% F1 (LogReg). Ogma's representations are better separated in the low-data regime — it achieves 100% precision with LogReg, meaning zero false positives.

3. Prompt Injection Detection — neuralchemy/Prompt-injection-dataset

Dataset: neuralchemy/Prompt-injection-dataset — Binary injection detection Train: 4,391 · Test: 942