VectraYX-Nano

A 42M-parameter Spanish cybersecurity language model trained from scratch with curriculum learning and native MCP tool use.

Key Results (VectraYX-Bench)

Model	Params	B1 KW	B2 F1	B3 TM	B4 Tool	B5
VectraYX-Nano v2 (N=4 seeds)	42M	0.228 ± 0.079	0.196 ± 0.005	0.029 ± 0.040	0.000	0.775 ± 0.050
Nano + LoRA mini (N=4 seeds)	42M	0.011 ± 0.004	0.201 ± 0.002	0.021 ± 0.012	0.145 ± 0.046	0.575 ± 0.043
VectraYX-Base 260M	260M	0.325	0.220	0.114	0.000	0.800
Base + LoRA mini	260M	0.025	0.200	0.000	0.580	0.600
VectraYX-Pro 3B	3.2B	0.341	0.695	0.686	0.600	0.800
VectraYX-Pro 7B	7B	0.335	0.815	0.686	0.880	0.800

Key Finding

The B4=0.000 floor in mixed SFT is a corpus-density artifact, not a capacity gate. At ratio 1:21 (2,801 tool-use examples), Nano 42M achieves B4=0.145 ± 0.046 and Base 260M achieves B4=0.580.

Usage

# Load with custom inference script
# See: https://huggingface.co/vectrayx/vectrayx-paper-code

from huggingface_hub import hf_hub_download
import torch

# Download checkpoint
ckpt_path = hf_hub_download("vectrayx/vectrayx-nano", "nano_sft_v5.pt")
tokenizer_path = hf_hub_download("vectrayx/vectrayx-nano", "tokenizer/vectrayx_bpe.model")
config_path = hf_hub_download("vectrayx/vectrayx-nano", "configs/nano.json")

Citation

@inproceedings{santillana2026vectrayx,
  title     = {VectraYX-Nano: A 42M-Parameter Spanish Cybersecurity Language Model
               with Curriculum Learning and Native Tool Use},
  author    = {Santillana, Juan S.},
  booktitle = {Preprint},
  year      = {2026}
}