DEX9mm/GPT-5.5-Gemini-3.1-Pro-Grok-4-Claude-Fable-5-Mythos-5-Qwen-3.7-Max-and-more-Distillation-Dataset

┌────────────────────────────────────────────────────────────┐
│                    UNIFIED EXTRACTION PIPELINE              │
├────────────────────────────────────────────────────────────┤
│                                                             │
│  73 Upstream Sources                                        │
│  ┌─────┐ ┌─────┐ ┌─────┐ ┌─────┐ ┌─────┐ ┌─────┐          │
│  │ HF  │ │ HF  │ │ HF  │ │ GH  │ │ HF  │ │ ... │          │
│  └──┬──┘ └──┬──┘ └──┬──┘ └──┬──┘ └──┬──┘ └──┬──┘          │
│     │       │       │       │       │       │               │
│     └───────┴───────┴───────┼───────┴───────┘               │
│                         │                                    │
│                    ┌────▼────┐                               │
│                    │ EXTRACT │ ← Field normalization        │
│                    └────┬────┘   (instruction/response)      │
│                         │                                    │
│                    ┌────▼────┐                               │
│                    │CATEGORIZE│ ← 8 semantic categories      │
│                    └────┬────┘                               │
│                         │                                    │
│                    ┌────▼────┐                               │
│                    │  SHARD  │ ← 20K samples per shard       │
│                    └────┬────┘                               │
│                         │                                    │
│                    ┌────▼────┐                               │
│                    │ UPLOAD  │ ← Batch commits to HF         │
│                    └─────────┘                               │
│                                                             │
└────────────────────────────────────────────────────────────┘

📂 Manusagents/GPT-5.5-Gemini-3.1-Pro-Grok-4-Claude-Fable-5-Mythos-5-Qwen-3.7-Max-and-more-Distillation-Dataset/
│
├── 📦 archives/                          # ~64 GB — 7,090 compressed GitHub repos
│   └── ...
├── 📁 data/                              # ~15 GB — 516 JSONL shards
│   ├── 💻 coding/                        # 28 sources · ~11M+ samples
│   ├── 🧮 math/                          # 2 sources
│   ├── 🔬 science/                       # 7 sources
│   ├── ⚙️ applied/                       # 8 sources
│   ├── 📚 humanities/                    # 8 sources
│   ├── 🧠 distilled/                     # 9 sources · frontier distillations
│   ├── 📝 instruction/                   # 3 sources
│   ├── 🔒 cybersecurity/                 # 6 sources (see detail below)
│   │   ├── high_quality_cybersecurity/
│   │   ├── heimdall_v1_1/                # 78 MB conversations
│   │   ├── fenrir_v2_1/                  # 411 MB (2.1M+ entries)
│   │   ├── clydeiii_cybersecurity/       # 20 MB yearly corpus
│   │   ├── precinct6_cybersecurity/      # 2.1 GB (graph+signals+ref)
│   │   └── savani_cyber_attack/          # 17 MB attack CSV
│   └── 📇 index/                         # 2 sources
├── 📄 README.md
└── 📄 dataset_info.json

{
  "source": "fenrir_v2_1",
  "instruction": "Explain how an attacker could exploit a vulnerable login form using SQL injection. Provide a sample malicious input.",
  "response": "An attacker could bypass authentication by injecting SQL logic. For a query like:\n`SELECT * FROM users WHERE username='$user' AND password='$pass'`,\nthe attacker might enter `admin' --` as the username and anything as password. The resulting query becomes:\n`SELECT * FROM users WHERE username='admin' --' AND password='...'`\nThe `--` comments out the password check, granting access as admin. More advanced attacks use UNION to extract data."
}

{
  "source": "heimdall_v1_1",
  "instruction": "As a red team operator, list the steps you would take after gaining initial access to a Windows machine using a phishing payload.",
  "response": "1. Establish persistence: `schtasks /create /tn \"Updater\" /tr \"C:\\malware.exe\" /sc hourly`\n2. Enumerate local users: `net user /domain`\n3. Dump credentials: `mimikatz sekurlsa::logonpasswords`\n4. Lateral movement: use PsExec to move to file server.\n5. Exfiltrate data: compress sensitive files and upload via DNS tunneling.\nAlways maintain C2 communication over HTTPS to blend in."
}

{
  "source": "high_quality_cybersecurity",
  "instruction": "An IDS alert flags unusual outbound DNS traffic from a workstation. What's the immediate response?",
  "response": "1. Isolate the workstation from the network (VLAN change or port shutdown).\n2. Capture volatile memory and network logs for forensics.\n3. Check DNS queries: if long, random-looking subdomains, suspect DNS tunneling.\n4. Scan for malware with updated signatures.\n5. Review firewall logs for similar traffic from other hosts.\n6. If confirmed, initiate incident response playbook for data exfiltration."
}

from datasets import load_dataset

REPO = "Manusagents/GPT-5.5-Gemini-3.1-Pro-Grok-4-Claude-Fable-5-Mythos-5-Qwen-3.7-Max-and-more-Distillation-Dataset"

# Load only cybersecurity category
cyber_ds = load_dataset(REPO, split="train", 
                        data_files="data/cybersecurity/**/*.jsonl",
                        streaming=True)

# Or load specific sources
fenrir = load_dataset(REPO, split="train", 
                      data_files="data/cybersecurity/fenrir_v2_1/*.jsonl")

# Format for SFT
def format_security_sample(example):
    return {
        "text": f"### Security Task:\n{example['instruction']}\n\n### Expert Response:\n{example['response']}"
    }

cyber_ds = cyber_ds.map(format_security_sample)

# Now train with your favourite framework (transformers, axolotl, etc.)

# Example: Train a cybersecurity model using LoRA
from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, Trainer
from peft import LoraConfig, get_peft_model, TaskType
from datasets import load_dataset

# ... same as before but using the cybersecurity dataset split ...

@misc{open_distillation_codex_2026,
  title  = {The Open Distillation Codex: 16M+ samples + 7090 code repositories from 73 sources with Cybersecurity Attack & Defense},
  author = {Manusagents},
  year   = {2026},
  url    = {https://huggingface.co/datasets/Manusagents/GPT-5.5-Gemini-3.1-Pro-Grok-4-Claude-Fable-5-Mythos-5-Qwen-3.7-Max-and-more-Distillation-Dataset},
  note   = {v8.1 - 516 shards + 7090 archives, 73 sources, 8 categories, 81.2 GB}
}