VectraYX-Nano is a 42M-parameter Spanish cybersecurity language model trained from scratch with curriculum learning and native Model Context Protocol (MCP) tool use. It is, to our knowledge, the first published Spanish-native cybersecurity LLM with end-to-end MCP integration.

arXiv

Zenodo

Paper: VectraYX-Nano: A 42M-Parameter Spanish Cybersecurity Language Model with Curriculum Learning and Native Tool Use
Repository: vectrayx/vectrayx-nano-paper
arXiv DOI: https://doi.org/10.48550/arXiv.2605.13989
Author website: https://jsantillana.com

Released Model: VectraYX-Nano v7 (Headline)

VectraYX-Nano v7 is the released headline model. It uses the same 42M architecture and three-phase curriculum pre-training as the v2 bootstrap-ablation reference, with the SFT corpus rebalanced to a tool-use ratio of 1:21 (vs. 1:211 in v2). This single change raises B4 (tool-selection) from 0.000 to 0.230 ± 0.052 across N=4 seeds while retaining strong CVE recall (B1=0.332±0.005) and conversational quality (B5=0.725±0.130).

Files in this repo:

File	Description
`nano_sft_v7_s42.pt`	Nano v7 seed 42 — recommended for inference
`nano_sft_v5.pt`	Nano v2 (mixed SFT, bootstrap-ablation reference)
`vectrayx-nano-f16.gguf`	F16 GGUF — run with llama.cpp / Ollama
`lora/nano_lora_mini_s{42,7,13,23}.pt`	LoRA adapters (tool-use density study, ratio 1:21)
`tokenizer/vectrayx_bpe.model`	BPE-16384 tokenizer
`configs/nano.json`	Nano 42M architecture config
`configs/base.json`	Base 260M architecture config

Key Results (VectraYX-Bench, N=4 seeds)

Model	Params	B1 KW	B2 F1†	B3 TM	B4 Tool	B5 Chat
VectraYX-Nano v7 (headline)	42M	0.332±0.005	—	—	0.230±0.052	0.725±0.130
VectraYX-Nano v2 (bootstrap ablation)	42M	0.226±0.065	0.199±0.004	0.029±0.035	0.000	0.775±0.043
Nano LoRA mini (ratio 1:21, N=4)	42M	0.011±0.004	0.201±0.002	0.021±0.012	0.145±0.046	0.575±0.043
SmolLM2-135M + LoRA-32	135M	0.334	0.225	0.143	0.160	0.800
VectraYX-Base 260M	260M	0.325	0.220	0.114	0.000	0.800
Base 260M LoRA mini (ratio 1:21, N=4)	260M	0.019±0.003	0.203±0.002	—	0.445±0.201	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
GPT-4o (frontier reference)	—	0.333	0.110	0.520	0.615	0.631

†B2 is a benchmark artifact in this revision (key mismatch in harness, fix queued).

B5 inversion: Nano v7 (0.725±0.130) and Nano v2 (0.775±0.043) both exceed GPT-4o (0.631) on the 314-prompt held-out chat suite — the register-matched bootstrap corpus makes conversational Spanish the model's first language.

Key Findings

1. Loss-vs-register inversion. A higher-perplexity bootstrap corpus (OpenSubtitles-ES) yields better post-SFT chat behavior than a lower-perplexity alternative (mC4-ES). At the nano scale, the bootstrap corpus dictates the model's default response style; SFT cannot fully overwrite it.

2. Tool-use is corpus-density-gated, not capacity-gated. The B4=0.000 floor in the mixed SFT (ratio 1:211) is a corpus-density artifact. Rebalancing to 1:21 (2,801 tool-use examples) shifts the first-token prior to <|tool_call|> and raises B4 to 0.230±0.052 at 42M — without retraining the backbone.

Inference: llama.cpp / Ollama (GGUF)

bash

# With Ollama
ollama run hf.co/jsantillana/vectrayx-nano:vectrayx-nano-f16.gguf

# With llama.cpp
./llama-cli -m vectrayx-nano-f16.gguf \
  --chat-template llama3 \
  -p "<|system|>Eres VectraYX, asistente experto en ciberseguridad para LATAM.<|end|>" \
  -i

Runs at 6–10 tok/s on Raspberry Pi 4 and 60–100 tok/s on a laptop CPU.

Inference: PyTorch

python

from huggingface_hub import hf_hub_download
import torch, json, sys

sys.path.insert(0, ".")  # needs training/transformer.py from vectrayx-paper-code

ckpt = hf_hub_download("jsantillana/vectrayx-nano", "nano_sft_v7_s42.pt")
tok  = hf_hub_download("jsantillana/vectrayx-nano", "tokenizer/vectrayx_bpe.model")
cfg  = hf_hub_download("jsantillana/vectrayx-nano", "configs/nano.json")

Full inference script at vectrayx-paper-code.

Training Details

Component	Details
Parameters	41.95M
Architecture	Transformer decoder, GQA (8q/2kv), QK-Norm, RMSNorm, SwiGLU, RoPE, z-loss
Tokenizer	BPE-16384, byte-fallback, 50/50 conv/tech balance
Pre-training	170M tokens, 3-phase curriculum with 25% replay buffer
SFT (v7)	13K OASST1-ES + 4K CVE Q&A + 2.8K tool-use (ratio 1:21)
Hardware	GCP L4 24GB (pre-training) + AWS g4dn.xlarge T4 16GB (multi-seed SFT)
Cost	~$29 USD total (corpus + training)

Citation

bibtex

@misc{santillana2026vectrayx,
  title     = {VectraYX-Nano: A 42M-Parameter Spanish Cybersecurity Language Model
               with Curriculum Learning and Native Tool Use},
  author    = {Santillana, Juan S.},
  year      = {2026},
  eprint    = {2605.13989},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CL},
  url       = {https://arxiv.org/abs/2605.13989}
}