Models¶

Every model Olaverse ships, in one place — grouped by task, with guidance on which variant to pick.

Family	Task	Page
DiacNet	Diacritization (restore accents, tones, special characters)	DiacNet →
LID	Language detection (5–25 languages)	Language Detection →
OTK-BPE	Tokenization (Nigerian languages, Swahili, Kinyarwanda)	Tokenizers →
Reranker / Embedder	Retrieval, RAG, cross-lingual search	Retrieval →
MIST	General-purpose LLMs (8B–140B)	MIST →
LegalPeace	Legal contract reasoning	LegalPeace →
Prism	Image upscaling, denoising, steganography	Prism →

Full Model Index¶

Model	Task	Size	Speed	Install
`LIDLite5`	Language ID (5 langs)	1.1 MB JSON	0.014 ms	`olaverse`
`LIDNeural5`	Language ID (5 langs)	484 MB	13 ms	`olaverse[deeplearning]`
`LIDLite25`	Language ID (25 langs)	~5-10 MB	<1 ms	`olaverse[lid]`
`LIDNeural25`	Language ID (25 langs)	~500 MB	—	`olaverse[deeplearning]`
`LIDNeural5_1`	Language ID (4 Nigerian langs, no English)	~120 MB	—	`olaverse[deeplearning]`
`MIST-Mini-8B`	General LLM	15 GB	~63 tok/s	`olaverse[deeplearning]`
`MIST-1-70B`	General LLM	132 GB	~23 tok/s	hosted or multi-GPU
`MIST-1-140B`	General LLM	256 GB	~8 tok/s	hosted or 2× H200
`MIST-Mini-8B-Thinking`	Reasoning LLM	15 GB	~55 tok/s	`olaverse[deeplearning]`
`LegalPeace`	Legal reasoning	7B (4-bit)	—	`olaverse[legal]`
`DiacNet` (5 Yoruba/Igbo variants)	Diacritization	1 MB – 503 MB	—	`olaverse` / `[deeplearning]`
`diacnet-1.0`	Diacritization (10 langs)	~300 MB	Slow	`olaverse[deeplearning]`
`OTK-BPE-50k` (5 Nigerian variants)	Tokenization	~3 MB each	—	`olaverse`
`OTK-BPE` (9 Swahili/Kinyarwanda/merged variants)	Tokenization	varies	—	`olaverse`
`Reranker` (2 sizes)	Reranking	23 MB – 150M params	—	`olaverse[retrieval]`
`Embedder`	Sentence embeddings (ha/yo/ig)	~120 MB	—	`olaverse[retrieval]`
`PrismUpscaler` (3 sizes)	Image super-resolution	~25K params – small	—	`olaverse[vision]`
`PrismDenoiser`	Image denoising	Small U-Net	—	`olaverse[vision]`
`PrismSteganography`	Image steganography	Small U-Net	—	`olaverse[vision]`

Which model should I use?¶

Diacritization¶

Need	Model
Fast Yoruba, CPU-only	`diacnet-yor-viterbi`
Highest Yoruba accuracy	`diacnet-yor-x`
Igbo	`diacnet-ig`
10 languages, one model	`diacnet-1.0`
Automatic language routing	`Diacritizer(model="auto")`

Language detection¶

Need	Model
Nigerian languages + English, zero GPU	`LIDLite5`
Nigerian languages + English, best accuracy	`LIDNeural5`
25 languages, CPU-only	`LIDLite25`
25 languages, best short-text accuracy	`LIDNeural25`
Nigerian languages only (input never contains English)	`LIDNeural5_1`

LLMs¶

Need	Model
Fast everyday use, single consumer GPU	`MIST(size="8b")`
Structured, detailed output	`MIST(size="70b")`
Deepest reasoning	`MIST(size="140b")`
Step-by-step visible reasoning	`MIST(size="thinking")`
Contract analysis	`LegalPeace`

API Conventions¶

Olaverse model classes follow a consistent verb pattern — once you know one model, you know them all:

Method	Used by	Meaning
`predict()` / `predict_proba()`	LID models	Classify input, optionally with probabilities
`predict_batch()`	LID models	Batched classification in one forward pass
`restore()`	`Diacritizer`	Restore diacritics to plain text
`encode()` / `decode()`	`Tokenizer`, `Embedder`	Text ↔ token IDs / embedding vectors
`rank()` / `score()`	`Reranker`	Order or score (query, passage) pairs
`normalize()`	`TTSNormalizer`, `NaijaNormalizer`	Expand numbers/abbreviations for speech
`generate()` / `chat()`	`MIST`, `LegalPeace`	LLM completion / multi-turn chat
`load()`	neural models	Explicit one-time weight download (lazy elsewhere)

Small CPU models (e.g. LIDLite5, Viterbi diacritizers) load instantly at construction; neural models expose an explicit load() so the download happens where you expect it.