Model reference · open weights
ogma-small 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) | 9M |
| Context | 1k tokens |
| Runs with | ogma |
| Released | 2026-04-23 |
| Popularity | 557 downloads / month |
| Licence | Commercial licence needed |
About
Efficient English text embedding model for semantic search, RAG, vector search, retrieval, clustering, classification, STS, and agent memory — MTEB 56.32, 8.6M parameters, 1024-token context
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-small. This repository's own weights remain CC-BY-NC-4.0 and it no longer hosts that artifact.
Ogma Small is the flagship efficiency model in the family. At 8.6M parameters it scores 56.32 MTEB in our canonical 66-task Ogma paper results, while using only 38% of MiniLM-L6-v2's parameters, running 1.75× faster on CPU, and handling inputs 4× longer (1024 vs 256 tokens). Purpose-built to be the drop-in for every place you currently reach for MiniLM.
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-small is the default efficiency model for semantic search, RAG retrieval, agent memory, vector databases, document retrieval, text classification, clustering, STS / sentence similarity, and lightweight reranking pipelines. It is aimed at teams looking for a small, fast MiniLM-style embedding model with longer context and strong MTEB quality.
Good fits:
Choose ogma-small when you want the best balance of quality, speed, size, and deployability across edge, local, and server workloads.
[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-small | all-MiniLM-L6-v2 | Δ vs MiniLM |
|---|---|---|---|
| Classification | 66.49 | 62.62 | +3.87 |
| Clustering | 40.69 | 41.94 | -1.25 |
| PairClassification | 82.91 | 82.37 | +0.54 |
| Reranking | 50.51 | 58.04 | -7.53 |
| Retrieval | 42.05 | 41.95 | +0.10 |
| STS | 82.00 | 78.90 | +3.10 |
| Summarization | 29.59 | 30.81 | -1.22 |
| Overall | 56.32 | 56.09 | +0.23 |
ogma-small is the default recommendation for most use cases. It is MiniLM-class quality while being faster, smaller, and context-aware. Use ogma-base when you need the extra quality margin; use ogma-mini when you need to go sub-4M 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:
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 | 85.540 |
| classification | MTEB AmazonPolarityClassification | accuracy | 76.720 |
| clustering | MTEB RedditClustering | v_measure | 43.940 |
| pair-classification | MTEB TwitterSemEval2015 | cos_sim_ap | 68.490 |
| reranking | MTEB MindSmallReranking | map | 30.550 |
| retrieval | MTEB MSMARCO | ndcg_at_10 | 34.310 |
| summarization | MTEB SummEval | cos_sim_spearman | 29.590 |
Using it via the API
Once AxForge deploys ogma-small for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (ogma-small 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-small","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.