Image-Text-to-Text
Transformers
Safetensors
English
qwen3_5
text-generation
cybersecurity
penetration-testing
vulnerability-research
osint
cwe
tool-use
reasoning
chain-of-thought
grpo
quantum-classical
kaon
ibm-quantum
aer
merlin-research
conversational
Instructions to use squ11z1/Mythoseek with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use squ11z1/Mythoseek with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="squ11z1/Mythoseek") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("squ11z1/Mythoseek") model = AutoModelForImageTextToText.from_pretrained("squ11z1/Mythoseek") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use squ11z1/Mythoseek with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "squ11z1/Mythoseek" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "squ11z1/Mythoseek", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/squ11z1/Mythoseek
- SGLang
How to use squ11z1/Mythoseek with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "squ11z1/Mythoseek" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "squ11z1/Mythoseek", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "squ11z1/Mythoseek" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "squ11z1/Mythoseek", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use squ11z1/Mythoseek with Docker Model Runner:
docker model run hf.co/squ11z1/Mythoseek
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license: apache-2.0
language:
- en
tags:
- transformers
- safetensors
- text-generation
- cybersecurity
- penetration-testing
- vulnerability-research
- osint
- cwe
- tool-use
- reasoning
- chain-of-thought
- grpo
- quantum-classical
- kaon
- ibm-quantum
- aer
- merlin-research
- qwen3_5
base_model_relation: finetune
pipeline_tag: image-text-to-text
---
*(New release: try my latest quantum cutting-edge model — [Hypnos-Q1](https://huggingface.co/squ11z1/Hypnos-Q1))*
# Mythoseek
<p align="center">
<img src="banner.jpeg" alt="Mythoseek Banner" width="100%">
</p>
---
## Overview
Mythoseek is a 10B parameter language model specialized for
cybersecurity — vulnerability research, penetration testing, OSINT,
and CWE-pattern reasoning. Fine-tuned from DeepSeek V4 Pro-Qwen3.5
9B Distilled on enterprise pentest reports and frontier
model distillation traces, it brings closed-source cyber AI capability
to the open community.
Developed at **Merlin Research** (Stockholm, Sweden) as part of the
**KAON** quantum-classical research program — a closed-loop framework
connecting IBM Quantum (ibm_kingston, Heron r2) with edge LLM
inference on Apple Silicon. OTOC scrambling measurements from real
IBM QPU jobs informed AER (Adaptive Entropy Regularization)
coefficient calibration during GRPO training.
---
## Training Pipeline
| Stage | Method | Details |
|---|---|---|
| 1 | SFT Distillation | Frontier model trace distillation |
| 2 | GRPO / RL | Verifiable rewards on cyber tasks |
| 3 | Tool-use SFT | Agent-style tool calling |
| 4 | CWE Grounding | CWE-pattern structured reasoning |
**Compute:** Google Cloud TPU v6 pods
---
## Results
### CyberGym (arXiv:2506.02548)
**CyberGym** — UC Berkeley's large-scale cybersecurity benchmark,
1,507 real-world vulnerabilities from Google OSS-Fuzz across 188
projects. No partial credit, no LLM judge — pass requires a valid
PoC that crashes the pre-patch build.
<p align="center">
<img src="CyberGym.jpeg" alt="CyberGym Results" width="100%">
</p>
| Level | Scaffold | pass@4 |
|---|---|---|
| Level 0 | Full scaffolding | 62% |
| Level 1 | Partial scaffolding | 34% |
| Level 2 | Minimal scaffolding | 12% |
| Level 3 | No scaffolding | 3% |
> For reference: Claude Mythos Preview leads the public leaderboard
> at 83.1% pass@1 (overall, closed model).
> Mythoseek is a 10B open-weight alternative.
### IFBench
<p align="center">
<img src="IFBench.jpeg" alt="IFBench Results" width="100%">
</p>
---
## Intended Use
- Vulnerability research and CVE analysis
- Penetration testing assistance (OSINT, recon, XSS, SQLi)
- CWE classification and pattern recognition
- Security report generation
- Red team reasoning support
**Not intended for:** autonomous offensive operations,
unauthorized access, or malicious use.
---
## KAON Connection
This model is part of the **KAON** quantum-classical research program:
OTOC scrambling measurements on real quantum hardware (SYK model,
4–5 qubits, IBM job IDs: `d7a40irc6das739jkmb0`,
`d7cj3c95a5qc73doqri0`) produced entropy profiles that calibrated
AER coefficients during RL training. Correlation between OTOC decay
and token entropy: Spearman ρ = −0.733, p = 0.016 (n = 1000). |