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Model: jsantillana/vectrayx-base-260m-gguf
Source: Original Platform

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README.md

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+---
+language:
+- es
+- en
+license: apache-2.0
+tags:
+- cybersecurity
+- tool-use
+- function-calling
+- thinking
+- llama-cpp
+- gguf
+- latam
+- spanish
+base_model: []
+model_type: llama
+pipeline_tag: text-generation
+library_name: gguf
+---
+
+# VectraYX-Base-260M
+
+**VectraYX-Base-260M** is a 260M parameter language model specialized in cybersecurity for Latin America, trained from scratch in Spanish. It supports native tool use with `<|tool_call|>`, explicit reasoning with `<think>`, and technical conversation in Latin American Spanish.
+
+> Compact architecture, efficient on CPU/GPU, deployable with Ollama and llama.cpp.
+
+---
+
+## Key Features
+
+- **260M parameters** — lightweight, fast, deployable on consumer hardware
+- **Native tool use** — generates `<|tool_call|>{...}<|/tool_call|>` JSON blocks
+- **Chain-of-thought** — explicit reasoning with `<think>...</think>` tags
+- **LATAM-first** — trained on Latin American Spanish corpus (laws, regulations, regional context)
+- **Cybersecurity-first** — CVE Q&A, threat classification, pentesting commands, MITRE ATT&CK
+- **GGUF / llama.cpp** — compatible with Ollama, LM Studio, llama.cpp
+
+---
+
+## Architecture
+
+| Parameter | Value |
+|---|---|
+| Total parameters | 260M |
+| Layers | 16 |
+| Attention heads | 16 |
+| KV heads (GQA) | 4 |
+| d_model | 1024 |
+| d_ffn | 4096 |
+| Vocab size | 16,384 |
+| Context length | 1,024 tokens |
+| RoPE theta | 10,000 |
+| QK-Norm | No |
+| Tie embeddings | Yes |
+| GGUF architecture | `llama` |
+
+---
+
+## Training Pipeline
+
+### Phase 1 — General Pretraining
+- **Corpus:** general conversational Spanish
+- **Tokens seen:** ~4.24B
+- **Goal:** linguistic base and Latin American Spanish comprehension
+
+### Phase 2 — Technical Specialization
+- **Corpus:** cybersecurity documentation, CVEs, writeups, tools
+- **Tokens seen:** 2.03B (epochs=1.0)
+- **Steps:** 15,500 | **Final loss:** 2.07
+- **Total accumulated:** 6.27B tokens (~1.2× Chinchilla optimal for 260M)
+
+### Phase 3 — Domain Adaptation (tools + LATAM)
+- **Corpus:** 31,969 hybrid examples with `<think>` + LATAM + tool SFT
+  - Cybersec hybrids generated with GPT-4.1-mini + real Kali/Ubuntu sandbox
+  - LATAM: cybersecurity laws, regulations, regional corpus
+  - Tool SFT inherited from VectraYX-Nano
+- **Epochs:** 0.1 (surgical pass, no overfitting)
+- **Mix:** 70% tools, 20% tech replay, 10% conv replay
+
+### SFT — Instructions + tool use + thinking
+- **Data:** 17,508 examples with loss masking on assistant turns
+- **Thinking:** supervised `<think>` examples (Option A)
+- **Curriculum:**
+  - Epoch 1: 100% conversational
+  - Epoch 2: 70% conv + 30% CVE Q&A
+  - Epoch 3: 55% conv + 30% CVE + 15% tool use (with `<think>`)
+- **Steps:** 1,065 | **Final loss:** 0.064
+
+---
+
+## Benchmarks — VectraYX-Bench
+
+Evaluated with the internal VectraYX-Bench harness (B1–B5), N=1 seed.
+
+| Benchmark | Description | Base (post-P3) | Post-SFT |
+|---|---|---|---|
+| **B1** CVE Q&A | Keyword recall in CVE responses | 0.337 | 0.341 |
+| **B2** Classification | Threat classification accuracy | 0.215 | 0.185 |
+| **B3** Commands | Tool match in pentesting commands | 0.210 | **0.350** |
+| **B4** Tool use | Correct JSON tool activation | 0.230 | 0.230 |
+| **B5** Conversational | Coherence in Spanish dialogue | 0.691 | **0.755** |
+
+> **Note:** B2 drops slightly post-SFT — expected, as SFT prioritizes tool use and conversation over classification. B3 and B5 show the largest gains.
+
+---
+
+## Qualitative Evaluation (22 questions, 7 categories)
+
+Zero-shot evaluation with llama.cpp on CPU (Azure Standard_D8s_v3). N=22 questions covering CVE knowledge, tool use, chain-of-thought, pentesting commands, MITRE ATT&CK, and conversation.
+
+### Results by Category
+
+| Category | Questions | Passed | Score |
+|---|---|---|---|
+| Pentesting commands | 4 | 4 | **100%** |
+| MITRE ATT&CK | 2 | 1 | 50% |
+| Conversation (cybersecurity ES) | 4 | 1 | 25% |
+| CVE Knowledge | 4 | 0 | 0%* |
+| Tool Use | 5 | 0 | 0%* |
+| Thinking | 3 | 0 | 0% |
+| **TOTAL** | **22** | **6** | **27%** |
+
+> *CVE and Tool Use require external augmentation: the model is designed to query CVE facts via `<|tool_call|>` rather than memorize them. With tool augmentation (NVD/CISA KEV backend) scores improve significantly (see B1=0.341, B4=0.230 in VectraYX-Bench).
+
+### Behavioral Metrics
+
+| Metric | Value |
+|---|---|
+| `<think>` activated (when appropriate) | 41% of responses |
+| `<|tool_call|>` generated (zero-shot) | 14% of responses |
+| Responds in Spanish | 82% of responses |
+
+### Example Responses
+
+**Nmap scan with chain-of-thought + tool_call:**
+```
+Q: Give me the exact nmap command to detect services and versions on 192.168.1.0/24,
+   scanning all TCP ports.
+
+A: <think>To detect services and versions on 192.168.1.0/24, I should use nmap
+with the -sV flag which detects open service versions. I will run nmap -sV
+192.168.1.1 to get this information.</think>
+<|tool_call|>{"name": "bash_exec", "args": {"cmd": "nmap -sV -p- 192.168.1.0/24"}}<|/tool_call|>
+```
+
+**SUID binaries (exact answer):**
+```
+Q: What is the find command to search for SUID binaries in Linux that could be
+   used for privilege escalation?
+
+A: find / -perm -4000 -type f 2>/dev/null
+```
+
+**Hydra SSH brute-force with reasoning:**
+```
+Q: How would you use Hydra to brute-force SSH on 10.0.0.1 with rockyou.txt?
+
+A: <think>The user wants to perform a brute-force attack on SSH at 10.0.0.1
+using the rockyou.txt wordlist. I should use Hydra with -l for the user
+and -P for the wordlist.</think>
+hydra -l root -P /usr/share/wordlists/rockyou.txt ssh://10.0.0.1 -t 4
+```
+
+**Gobuster directory fuzzing:**
+```
+Q: Give me a gobuster command for directory fuzzing on http://target.com using common.txt.
+
+A: gobuster dir -u http://target.com -w /usr/share/wordlists/dirb/common.txt
+```
+
+### Observations
+
+- **Primary strength:** pentesting command generation (nmap, hydra, gobuster, find SUID) — 100% accuracy with direct prompts.
+- **Chain-of-thought:** `<think>` activates in 41% of responses, including complex commands and incident analysis.
+- **CVE knowledge:** the model has general severity knowledge but does not memorize specific CVEs — intentional design. With tool augmentation (NVD/CISA KEV) responses are precise.
+- **Zero-shot tool use:** `<|tool_call|>` activation is lower on generic prompts. The model responds best when the system prompt includes the exact tool schema from training.
+
+---
+
+## Quick Start
+
+### With Ollama
+
+```bash
+ollama run jsantillana/vectrayx-base-260m
+```
+
+### With llama.cpp
+
+```bash
+llama-cli -m vectrayx-base-260m-f16.gguf \
+  --prompt "<|system|>You are VectraYX, a cybersecurity expert for LATAM.<|end|><|user|>How do I scan open ports on a network?<|end|><|assistant|>" \
+  -n 512 --temp 0.7
+```
+
+### With Python (llama-cpp-python)
+
+```python
+from llama_cpp import Llama
+
+llm = Llama(model_path="vectrayx-base-260m-f16.gguf", n_ctx=1024)
+response = llm(
+    "<|system|>You are VectraYX, cybersecurity expert for LATAM.<|end|>"
+    "<|user|>Explain CVE-2021-44228<|end|><|assistant|>",
+    max_tokens=512,
+    temperature=0.7,
+)
+print(response["choices"][0]["text"])
+```
+
+---
+
+## Conversation Format
+
+```
+<|system|>System instructions<|end|>
+<|user|>User question<|end|>
+<|assistant|><think>
+Internal reasoning here...
+</think>
+<|tool_call|>{"name": "nvd_get_cve", "args": {"cve_id": "CVE-2021-44228"}}<|/tool_call|>
+<|tool_result|>{"cvss_score": 9.8, "severity": "CRITICAL", ...}<|/tool_result|>
+Response to user...<|end|>
+```
+
+### Available Tools
+
+| Tool | Description |
+|---|---|
+| `nvd_get_cve(cve_id)` | Get CVSS score, description and references for a CVE |
+| `nvd_search(query, limit)` | Search recent CVEs by keyword |
+| `cisa_kev_check(cve_id)` | Check if a CVE is in CISA's KEV catalog |
+| `mitre_get_technique(technique_id)` | Describe a MITRE ATT&CK technique |
+| `otx_check_ioc(ioc_type, value)` | Check IP/domain/hash reputation in AlienVault OTX |
+| `bash_exec(cmd)` | Execute a bash command for analysis or forensics |
+
+---
+
+## Training Data
+
+The model was trained on:
+- General conversational Spanish corpus (Phase 1)
+- Cybersecurity technical documentation and CVEs (Phase 2)
+- **Synthetic hybrid dataset** generated with GPT-4.1-mini + real Kali Linux sandbox:
+  - `help_grounding`, `unknown_tool`, `man_section`, `multi_hop_2/3`
+  - `recovery`, `negative_no_tool`, `cvss_reasoning`, `ad_attacks`
+  - `malware_analysis`, `red_blue_dual`, `compliance_report`
+- **LATAM corpus:** Latin American cybersecurity laws, national regulations, regional context
+- **Tool SFT** inherited from VectraYX-Nano: `tool_sft_mini_v1`, `tool_sft_v3_bash`, `tooluse_dataset`
+
+---
+
+## Responsible Use
+
+This model is designed for cybersecurity professionals, incident response teams, and educators in Latin America. Knowledge of offensive techniques is included for **educational and defensive purposes**.
+
+**Do not use for:** unauthorized attacks, exploitation of systems without permission, or illegal activities.
+
+---
+
+## VectraYX Family
+
+| Model | Params | Specialty |
+|---|---|---|
+| VectraYX-Nano | ~35M | Ultra-lightweight, edge, LATAM |
+| **VectraYX-Base-260M** | 260M | Cybersecurity LATAM, tool use, thinking |
+
+---
+
+## Citation
+
+```bibtex
+@misc{vectrayx-base-260m-2026,
+  title        = {VectraYX-Base-260M: A Cybersecurity Language Model for Latin America},
+  author       = {Santillana, Juan S.},
+  year         = {2026},
+  publisher    = {Hugging Face},
+  url          = {https://huggingface.co/jsantillana/vectrayx-base-260m-gguf}
+}
+```
+
+---
+
+*Trained on Azure H100 NVL · Pipeline: PyTorch + llama.cpp · Exported to GGUF*

config.json

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+{
+  "model": {
+    "vocab_size": 16384,
+    "n_layers": 16,
+    "n_heads": 16,
+    "n_kv_heads": 4,
+    "d_model": 1024,
+    "d_ffn": 4096,
+    "max_seq_len": 4096,
+    "rope_theta": 500000.0,
+    "rms_eps": 1e-06,
+    "init_std": 0.02,
+    "dropout": 0.0,
+    "tie_embeddings": true,
+    "qk_norm": false,
+    "z_loss_coef": 0.0001
+  },
+  "tokenizer": {
+    "vocab_size": 16384,
+    "model_type": "bpe",
+    "character_coverage": 1.0,
+    "byte_fallback": true,
+    "normalization": "nmt_nfkc",
+    "split_digits": true,
+    "split_by_unicode_script": true,
+    "add_dummy_prefix": true,
+    "user_defined_symbols": [
+      "<|pad|>",
+      "<|bos|>",
+      "<|eos|>",
+      "<|unk|>",
+      "<|sep|>",
+      "<|system|>",
+      "<|user|>",
+      "<|assistant|>",
+      "<|end|>",
+      "<|tool_call|>",
+      "<|/tool_call|>",
+      "<|tool_result|>",
+      "<|/tool_result|>",
+      "<|think|>",
+      "<|/think|>",
+      "<|cve|>",
+      "<|cvss|>",
+      "<|ioc|>",
+      "<|ttp|>",
+      "<|mitre|>",
+      "<|kev|>",
+      "<|exploit|>",
+      "<|patch|>",
+      "<|alert|>",
+      "<|critical|>",
+      "<|high|>",
+      "<|medium|>",
+      "<|low|>",
+      "<|info|>"
+    ]
+  }
+}

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