Who worked on it.
CorX Labs is a one-person research lab. Everything published here — the model, the training pipeline, the documentation — was built by the developer below.
CorX1.5 was built by Nathan, a 14-year-old developer from Jamaica and the founder of CorX Labs. He designed the architecture, trained the tokenizer, wrote the pretraining and fine-tuning pipelines, curated the data and ran every training job — solo, on a single GPU, from random weights. There is no team behind it and no base model underneath it.
Nathan
Founder & AI Researcher · Jamaica · Age 14
Nathan started building with AI models before he could build one. At twelve he was experimenting with APIs, wiring up a chatbot on top of Gemini and customising its behaviour himself. It worked — but it was somebody else's model wearing his configuration, and that gap bothered him.
At thirteen he moved to fine-tuning, and it went badly. Slow GPUs, training runs that took longer than his patience, and eventually the decision to stop. That failure is the reason CorX1.5 exists: the next attempt skipped adaptation entirely and started from random weights.
CorX1.5 is the result — a custom decoder-only transformer with role embeddings and assistant-only loss masking, a 32,768-token tokenizer trained from scratch, and a three-stage pipeline covering pretraining, distillation and reasoning. Open-sourced under Apache 2.0, so anyone can download it and take it apart the same way he had to.
Worked on
- Model architecture — RoPE, SwiGLU, RMSNorm, role embeddings, assistant-only loss masking
- Custom 32,768-token BPE tokenizer
- Data curation across 25 open datasets, and the ~3B-token pretraining pipeline
- Distillation and automated quality filtering
- Reasoning training, including the step-by-step
<think>format - The hardcoded crisis-response safety layer
- Model card, documentation and ongoing checkpoint releases
The journey
Three attempts, one model.
The short version of how a teenager in Jamaica ended up with a language model trained from random weights.
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Age 12
APIs and a chatbot
Built a working chatbot on top of Gemini's API and customised it himself — prompts, behaviour, personality. Genuinely useful, and genuinely somebody else's model. The question that stuck: what would it take to make the model itself?
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Age 13
Fine-tuning, and giving up
Moved to fine-tuning existing base models. The hardware could not keep up — training runs dragged on for far too long on slow GPUs, and eventually he stopped. This is the part of the story most people leave out of their timeline. It is also the part that decided the next one.
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Age 14
From the ground up
Instead of adapting a model, he wrote one. A custom decoder-only transformer, a tokenizer trained from scratch, ~3 billion tokens of pretraining across 25 open datasets, then distillation and reasoning training. Debugged crash by crash on a single GPU. The result is CorX1.5.
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Now
Open-sourced, and still training
CorX1.5 is published under Apache 2.0 on Hugging Face, and it is not finished — new training data and targeted fixes go in as gaps turn up in testing, with updated checkpoints released as they are ready.
“For years I've shared a dream to build something great — my own AI. Today that dream came through, and I can say I made it from scratch, from the ground up. So I'm open-sourcing it for people like you to download.”
— Nathan, on releasing CorX1.5
Contributing
Want to work on a CorX model?
Use it and report back
The most useful contribution right now is finding where the model breaks. Run it, break it, and send what you found.
Data and evaluations
Open datasets worth training on, or an evaluation that would show something real about a model this size — both are welcome.
Just get in touch
Collaboration, mentorship, compute, or a question — one inbox, read by the person who wrote the code.