VectraYX-Nano: A 42M-Parameter Spanish Cybersecurity LLM with Native MCP Tool Integration

We present VectraYX-Nano, a 41.95M-parameter decoder-onlylanguage model trained from scratch in Spanish for cybersecurity,with a Latin-American regional focus and native tool invocationvia the Model Context Protocol (MCP). The model is built aroundfour contributions. (i) Corpus. VectraYX-Sec-ES, a 170M-tokenSpanish corpus assembled by an eight-VM distributed pipeline at∼$25 USD of cloud compute and partitioned into three curriculumphases: conversational (42M tokens, OpenSubtitles-ES [35] andOASST1 [33]), cybersecurity (118M tokens, NVD [39], Wikipedia-ES, in-house NVD-derived Spanish CVE mirror, security blogs),and offensive-security tooling (10M tokens, ExploitDB, HackTricks,OWASP). (ii) Architecture. A 42M-parameter Transformerdecoder combining Grouped-Query Attention [2], QK-Norm [14],RMSNorm [58], SwiGLU [49], RoPE [52], and a 𝑧-loss auxiliary [11],paired with a domain-balanced 16,384-token byte-fallbackBPE [34, 48] trained on a 50/50 conversational/technical mix-ture. (iii) Curriculum with replay. Continual pre-trainingacross the three phases with a replay buffer [30] mitigatescatastrophic forgetting [19, 32] and yields a monotonic lossdescent (9.80 → 3.17 → 3.00 → 2.16). After SFT (final loss 1.74)on a curriculum-aware mixture of OASST-ES, Alpaca-ES, CVEQ&A, and tool-use traces, the v2 bootstrap-ablation referenceattains a conversational gate of 0.775 ± 0.043 on B5 over 𝑁 = 4seeds (Section 8.6), and a controlled Phase-2 replay sweep over{0, 5, 10, 25, 50}% saturates B5 at ≥ 25% replay (Section 6.8).(iv) Two empirical findings, both N=4. A controlled bootstrap-corpus ablation across v2 (OpenSubs), v4 (mC4-ES [57]), andv6 (60/25/15 OpenSubs/mC4/Wiki) under 𝑁 = 4 seeds exposesa loss-versus-register inversion: the lower-perplexity bootstrapsyield measurably worse conversational behavior (𝑣2 > 𝑣4 > 𝑣6on B5 at every paired seed), indicating that at the nano scale thebootstrap-corpus register dominates downstream chat quality.The B4 (tool-selection) floor of 0.000 on the mixed SFT corpusis a corpus-density artifact, not a capacity gate: rebalancing theSFT mixture to tool-use ratio 1:21 yields VectraYX-Nano v7, thereleased headline configuration, which reaches B4 = 0.230 ± 0.052at 42M parameters (𝑁 = 4 seeds) while retaining B1 = 0.332 ± 0.005and B5 = 0.725 ± 0.130 (Section 8.7); a LoRA [29] replication on a260M from-scratch mid-tier reaches 0.445 ± 0.201. The releasedGGUF [22] artifact is 81 MB in F16 (approximately 20 MB in4-bit quantization), runs at sub-second time-to-first-token oncommodity hardware under llama.cpp [21], and is, to the best ofour knowledge, the first published Spanish-native cybersecurityLLM with end-to-end MCP integration. We release the corpusconstruction recipe, training scripts, configurations, GGUF weights,∗ The author is employed at Globant. Institutional affiliation approval is pending.and the B1–B5 benchmark suite (B1: 500, B2: 200, B3: 100, B4: 200,B5: 314 prompts) for reproducibility.