Model reference · open weights
ogma-micro is an open-weight embedding model from axiotic. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.
Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.
What it is
| Released by | axiotic |
|---|---|
| Type | Embedding models |
| Task | Embeddings |
| Parameters (lead) | 2M |
| Context | 1k tokens |
| Runs with | ogma |
| Released | 2026-04-23 |
| Popularity | 526 downloads / month |
| Licence | Commercial licence needed |
About
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.
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.
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:
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.
[SYM], or use [QRY]/[QRY] for retrieval (queries and documents both encoded with task="qry"); benchmark both routes on your taskBenchmarked with MTEB v2.10.7 on the standard 66-task English benchmark using category averaging (same methodology as the MTEB leaderboard).
| Category | ogma-micro | all-MiniLM-L6-v2 | Δ vs MiniLM |
|---|---|---|---|
| Classification | 59.53 | 62.62 | -3.09 |
| Clustering | 36.88 | 41.94 | -5.06 |
| PairClassification | 78.62 | 82.37 | -3.75 |
| Reranking | 49.74 | 58.04 | -8.30 |
| Retrieval | 33.09 | 41.95 | -8.86 |
| STS | 75.63 | 78.90 | -3.27 |
| Summarization | 31.77 | 30.81 | +0.96 |
| Overall | 52.18 | 56.09 | -3.91 |
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.
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.
Dataset: Arsive/toxicity_classification_jigsaw — Binary toxicity classification
Train: 25,960 · Test: 6,490
| Model | Classifier | Accuracy | F1 | Precision | Recall | AUC-ROC |
|---|---|---|---|---|---|---|
| Ogma | LogReg | 89.12% | 88.26% | 89.09% | 87.44% | 95.74% |
| Ogma | MLP | 88.91% | 87.98% | 89.14% | 86.85% | 95.92% |
| MiniLM | LogReg | 87.32% | 86.25% | 87.46% | 85.07% | 94.96% |
| MiniLM | MLP | 91.71% | 91.24% | 90.13% | 92.39% | 97.16% |
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.
Dataset: deepset/prompt-injections — Binary injection detection
Train: 546 · Test: 116 (low-data regime)
| Model | Classifier | Accuracy | F1 | Precision | Recall | AUC-ROC |
|---|---|---|---|---|---|---|
| Ogma | LogReg | 86.21% | 84.62% | 100.0% | 73.33% | 97.77% |
| Ogma | MLP | 90.52% | 90.27% | 96.23% | 85.0% | 98.1% |
| MiniLM | LogReg | 82.76% | 80.39% | 97.62% | 68.33% | 94.52% |
| MiniLM | MLP | 87.07% | 86.24% | 95.92% | 78.33% | 93.96% |
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.
Dataset: neuralchemy/Prompt-injection-dataset — Binary injection detection
Train: 4,391 · Test: 942
| Model | Classifier | Accuracy | F1 | Precision | Recall | AUC-ROC |
|---|---|---|---|---|---|---|
| Ogma | LogReg | 95.22% | 95.93% | 95.84% | 96.01% | 99.30% |
| *Ogma |
From the published model card. Full card on the HuggingFace links in the sidebar.
Benchmarks
As published on the model card — the maker's own numbers, not measured by AxForge.
| Task | Dataset | Metric | Score |
|---|---|---|---|
| sts | MTEB STSBenchmark | cosine_spearman | 77.820 |
| classification | MTEB AmazonPolarityClassification | accuracy | 67.630 |
| clustering | MTEB RedditClustering | v_measure | 37.830 |
| pair-classification | MTEB TwitterSemEval2015 | cos_sim_ap | 60.030 |
| reranking | MTEB MindSmallReranking | map | 30.100 |
| retrieval | MTEB MSMARCO | ndcg_at_10 | 21.780 |
| summarization | MTEB SummEval | cos_sim_spearman | 31.770 |
Using it via the API
Once AxForge deploys ogma-micro for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (ogma-micro 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":"ogma-micro","input":"text to embed"}'
Create an account — your API key is available in the console. 3M free tokens every 30 days with every new account.