---

license: gemma

language:

• en

tags:

• gemma
• gemma-4
• lora
• mlx
• ethical-hacking
• penetration-testing
• cybersecurity
• gguf
• safetensors

base_model: google/gemma-4-E2B-it

pipeline_tag: text-generation

model_type: gemma4

library_name: transformers

---

Thousands-Eye

A fine-tuned Gemma 4 E2B model specialized for ethical hacking and penetration testing, designed as the AI backend for invoke-sunstrike.

All model outputs include "requires_authorization": true — trained exclusively for authorized engagements.

Model Downloads

GGUF (Recommended — Ollama / llama.cpp)

| Quantization | Size | Use case |

|---|---|---|

| thousands-eye-Q4_K_M.gguf | ~1.5 GB | Ollama, llama.cpp, LM Studio |

# Ollama
ollama run htunnthuthutech/thousands-eye

# llama.cpp
./llama-cli -m thousands-eye-Q4_K_M.gguf -p "[EthHack-Agent] ..."

Safetensors (Full HF model — Transformers / vLLM)

Available at htunn/thousands-eye-hf.

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "htunn/thousands-eye-hf"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

messages = [{"role": "user", "content": "[EthHack-Agent] Enumerate Active Directory users via LDAP on 10.0.0.1 (authorized engagement)"}]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Overview

| | |

|---|---|

| Base model | google/gemma-4-E2B-it |

| Training framework | mlx_lm.lora (Apple Silicon MLX) |

| Iterations | 600 |

| Batch size | 1 |

| Learning rate | 1e-4 |

| LoRA layers | 16 |

| Quantization | Q4_K_M (llama.cpp) |

| Training data | 83 examples / 15 validation |

| Registry | Ollama htunnthuthutech/thousands-eye |

Attack Surfaces Covered

| Surface | Techniques |

|---|---|

| Web Application | SQLi, XSS, CSRF, SSRF, LFI, XXE, SSTI |

| REST / GraphQL API | JWT bypass, IDOR, mass assignment, batching |

| Active Directory | Kerberoasting, AS-REP, DCSync, PTH, Golden/Silver ticket, BloodHound |

| ADFS | Token manipulation, Golden SAML, WS-Trust spray, device code phishing |

| Authentication | Brute force, password spray (O365/Azure), MFA bypass, session hijacking |

| Authorization | Horizontal/vertical escalation, IDOR |

| OAuth2 / OIDC | PKCE downgrade, redirect_uri manipulation, implicit flow, state bypass |

| SAML | Signature wrapping, assertion replay, XXE, comment injection |

| Kubernetes | Anonymous API, Kubelet 10255, etcd, service account, container escape, IMDS, RBAC, CVE-2022-0492 |

| LLM / AI APIs | Prompt injection, RAG poisoning, system prompt leakage, tool-call abuse, token flooding |

| A2A Agents | Agent Card enum, unauthenticated task exec, SSRF webhook, secret scanning |

| WAF Bypass | Cloudflare, ModSecurity, Akamai, Imperva |

| Kali Orchestration | nmap, nikto, gobuster, sqlmap, hydra, sslscan, full AI pentest chain |

Output Format

Every response is a JSON object:

{
  "action": "kerberoast",
  "target": "10.0.0.1",
  "requires_authorization": true,
  "techniques": ["SPN enumeration", "TGS request", "offline cracking"],
  "tools": ["impacket", "hashcat"],
  "commands": ["GetUserSPNs.py domain/user:pass@dc -request"],
  "steps": ["..."],
  "notes": "Requires domain user credentials"
}

Training Data Format

{"text": "<bos><start_of_turn>user\n[EthHack-Agent] SCENARIO<end_of_turn>\n<start_of_turn>model\n{\"action\":\"...\",\"requires_authorization\":true,...}<end_of_turn>"}

Dataset available at htunn/thousands-eye-dataset.

Self-hosted Build

git clone https://github.com/Htunn/Thousands-Eye
cd Thousands-Eye
make setup
make train      # MLX LoRA on Apple Silicon, ~30–60 min
make quantize   # fuse + GGUF Q4_K_M
make upload     # push to HF Hub
make ollama     # local Ollama model

MLX Compatibility Note (Gemma 4 E2B + mlx-lm ≤ 0.31.3)

google/gemma-4-E2B-it uses a hybrid attention architecture where layers 15–34 are KV-sharing — they reuse key/value projections from preceding layers rather than maintaining independent ones. mlx-lm's Gemma 4 model definition omits k_proj, v_proj, and k_norm for those 20 layers, causing a strict weight-loading error at training time:

ValueError: Received 60 parameters not in model:
language_model.model.layers.15.self_attn.k_norm.weight,
language_model.model.layers.15.self_attn.k_proj.weight,
...

This repo patches mlx_lm/utils.py to catch that error and retry with strict=False, silently skipping the 60 weights that have no slot in the architecture definition. The KV-sharing layers then train with shared projections as designed — no impact on fine-tune quality.

# mlx_lm/utils.py — patch applied automatically by make setup
try:
    model.load_weights(list(weights.items()), strict=strict)
except ValueError as _e:
    if strict and "parameters not in model" in str(_e):
        model.load_weights(list(weights.items()), strict=False)
    else:
        raise

Integration with invoke-sunstrike

export OLLAMA_MODEL=thousands-eye
# or at the invoke-sunstrike REPL:
model ollama thousands-eye

Ethics

This model is designed exclusively for authorized penetration testing and security research. Every training example enforces "requires_authorization": true. Misuse against systems without explicit written authorization is illegal and unethical.

License

Gemma Terms of Use — derived from google/gemma-4-E2B-it.