Instructions to use squ11z1/Mythoseek with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Mythoseek

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.

CyberGym Results

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

IFBench Results

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).

Downloads last month: 52

Safetensors

Model size

10B params

Tensor type

BF16

F32

Model tree for squ11z1/Mythoseek

Quantizations

1 model

Paper for squ11z1/Mythoseek

CyberGym: Evaluating AI Agents' Real-World Cybersecurity Capabilities at Scale

Paper • 2506.02548 • Published Mar 24 • 1