CorX Labs

CorX Labs

Jamaica · corx-labs.com

CorX Labs is an independent AI research lab in Jamaica, and the lab that publishes this index. It has released 3 models, all with open weights: a 27B Jamaican Patois assistant, a singing voice synthesis model, and a small language model trained from random weights. All 3 are listed below.


Released

The models we made

Each one, what it is and what it is built from. Full model cards live in Our Products.

Open weightsTool calling33K context

CorX3.8-27B

Jamaica's first large open-weight LLM — a Jamaican Patois-speaking assistant fine-tuned from Qwen3.8-27B and fully merged.

Parameters
27B
Type
Language model
Architecture
Dense transformer
Context
32,768 tokens
Input
Text
Output
Text
Licence
Apache 2.0
Weights
Downloadable
Open weights1K context

CorX1.5

A 158M-parameter decoder-only transformer written and trained from scratch on a single GPU — architecture, tokenizer, pretraining and fine-tuning.

Parameters
157.8M
Type
Language model
Architecture
Dense transformer
Context
1,024 tokens
Input
Text
Output
Text
Licence
Apache 2.0
Weights
Downloadable
Open weights

TriStream-SVS

A singing voice synthesis model that builds the source-filter theory of the human voice into its architecture as a hard constraint — pitch, timbre and breath texture run as three parallel encoder streams, combined only in a late fusion trunk. Generates at 24 kHz through rectified flow matching in roughly 32 sampling steps.

Parameters
321.8M
Type
Singing voice synthesis
Architecture
3 parallel encoders + late fusion trunk, rectified flow decoder
Input
Text, Audio
Output
Audio
Licence
Apache 2.0
Weights
Downloadable

Benchmarks

Why there are no scores here

CorX Labs has not published benchmark figures

Every other lab in this index is listed with the numbers it published. CorX Labs has not run these evaluations on its own models, so it has no numbers to list — and it is not going to quote Qwen3.8-27B's scores as CorX3.8-27B's, estimate from model size, or put a figure in a column it did not measure. An index that made an exception for the lab that runs it would be worth nothing.

The specification rows are real and comparable, so the models can still be put side by side with anything else here on parameters, context, modalities, licence and cost. When the evaluations are run, the scores go in like everyone else's.

In the index

How they sit in the table

The same row every other model gets, so nothing about ours is presented differently.

AI models with context window, price per million tokens and published benchmark scores. Sortable by any column.
CorX3.8-27BCorX LabsCorX Labs33KApache 2.0
CorX1.5CorX LabsCorX Labs1KApache 2.0
TriStream-SVSCorX LabsSinging voice synthesisCorX LabsApache 2.0