Veyra AI
AI & ML interests
Building tiny English language models for practical local AI. Veyra AI focuses on CPU-friendly inference, function calling, tool use, Python-oriented small models, distillation, RLVR, and lightweight fine-tuning. The goal is to make compact models that are easy to run, inspect, adapt, and use in real workflows without large hardware.
Recent Activity
Welcome to Veyra AI
We build tiny English language models for fast local inference. We focus on compact, CPU-friendly models that are easy to run and fine-tune. Our work centres on general base models, function calling, Python-focused variants, distillation, RLVR, and benchmarks. Our vision is to have tiny models that can run on low-powered hardware while being able to reason and retrieve knowledge whether that be using tool calls or from memory, and it all be integrated into easy to use open source tools such as the Veyra CLI. Our models can be found on the Open SLM Leaderboard.
Models:
| Model | Description |
|---|---|
| Veyra2-Apricot-50M-Base | Second generation Veyra base model with 50M parameters. |
| Veyra-30M-Base | First generation Veyra model with Cosmopedia-heavy generation and 30M parameters. |
| Veyra-30M-Instruct | First generation instruction-tuned Veyra model with 30M parameters |
Note on Kairo models: These are experimental models used for architectural and dataset testing. They should not be treated as reliable general-purpose language models. None are currently available.
Benchmarks:
| Benchmark | Description |
|---|---|
| Sci-Cloze-900 | Cloze-style GCSE Combined Science benchmark for evaluating small base language models. |
Note on benchmarks: Some of our benchmarks were created or expanded with AI assistance (e.g. generating candidate questions and answers).
