VectraYX-Pro 7B is a QLoRA-32 adapter for Qwen2.5-7B-Instruct fine-tuned on the VectraYX Spanish cybersecurity SFT corpus (~93,500 examples). It is part of the VectraYX model family presented in the paper arXiv:2605.13989.

arXiv

Zenodo

This repo contains only the QLoRA adapter weights (~308 MB). You need to load them on top of Qwen/Qwen2.5-7B-Instruct.

Paper: VectraYX-Nano arXiv:2605.13989
Nano 42M (from-scratch headline model): jsantillana/vectrayx-nano
Pro 3B: jsantillana/vectrayx-pro-3b
Author website: https://jsantillana.com

Results (VectraYX-Bench, single seed)

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-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 ref.)	—	0.333	0.110	0.520	0.615	0.631

B4=0.880 — best tool-selection score in the VectraYX family. B2=0.815 (best threat classification).

What is this?

This adapter applies the VectraYX cybersecurity specialization to Qwen2.5-7B-Instruct:

SFT corpus: ~93,500 examples — 13K OASST1-ES + 4K CVE Q&A + 2.8K MCP tool-use traces + general cybersecurity Q&A
Training: QLoRA rank=32 (4-bit quantized base), 3 epochs, lr=2e-4 on AWS SageMaker (ml.g5.xlarge)
Language: Spanish (LATAM-focused)
Tool use: Native MCP <|tool_call|> emission (B4=0.880, highest in family)

Usage

python

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
import torch

# Load 4-bit quantized base model (~5 GB VRAM)
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_quant_type="nf4"
)
base_model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen2.5-7B-Instruct",
    quantization_config=bnb_config,
    device_map="auto"
)

# Load VectraYX QLoRA adapter on top
model = PeftModel.from_pretrained(base_model, "jsantillana/vectrayx-pro-7b")
tokenizer = AutoTokenizer.from_pretrained("jsantillana/vectrayx-pro-7b")

# Inference
messages = [{"role": "user", "content": "¿Cuáles son los CVEs más críticos relacionados con Log4j?"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Model family

Model	Backbone	Params	B4 Tool	B2 F1
VectraYX-Nano v7	from-scratch	42M	0.230±0.052	—
VectraYX-Base	from-scratch	260M	0.000*	0.220
VectraYX-Pro 3B	Qwen2.5-3B + LoRA-64	3.2B	0.600	0.695
VectraYX-Pro 7B	Qwen2.5-7B + QLoRA-32	7B	0.880	0.815

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}
}