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
```

Mythoseek / README.md

squ11z1

Update README.md

15edc33 verified 3 days ago

preview code

raw

history blame contribute delete

3.1 kB

	---
	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
	---

	# 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).