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
Update README: rename to Mythoseek, fix base model to DeepSeek V4 Pro distilled
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README.md
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base_model:
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base_model_relation: finetune
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pipeline_tag: text-generation
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---
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<p align="center">
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<img src="assets/banner.jpeg" alt="Mythoseek Banner" width="100%">
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</p>
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# Mythoseek-10B
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**squ11z1 · Merlin Research**
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> *Mythos* — after Claude Mythos, Anthropic's frontier cyber model.
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## Overview
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Mythoseek
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cybersecurity — vulnerability research, penetration testing, OSINT,
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and CWE-pattern reasoning. Fine-tuned from
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confidential enterprise pentest reports and frontier
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traces, it brings closed-source cyber AI capability
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community.
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Developed at **Merlin Research** (Stockholm, Sweden) as part of the
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**KAON** quantum-classical research program — a closed-loop framework
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> For reference: Claude Mythos Preview leads the public leaderboard
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> at 83.1% pass@1 (overall, closed model).
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> Mythoseek
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---
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base_model: deepseek-ai/DeepSeek-V4-Pro-Qwen3.5-9B-Distilled
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base_model_relation: finetune
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pipeline_tag: text-generation
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---
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# Mythoseek
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<p align="center">
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<img src="assets/banner.jpeg" alt="Mythoseek Banner" width="100%">
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</p>
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**squ11z1 · Merlin Research**
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> *Mythos* — after Claude Mythos, Anthropic's frontier cyber model.
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## Overview
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Mythoseek is a 10B parameter language model specialized for
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cybersecurity — vulnerability research, penetration testing, OSINT,
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and CWE-pattern reasoning. Fine-tuned from DeepSeek V4 Pro-Qwen3.5
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9B Distilled on confidential enterprise pentest reports and frontier
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model distillation traces, it brings closed-source cyber AI capability
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to the open community.
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Developed at **Merlin Research** (Stockholm, Sweden) as part of the
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**KAON** quantum-classical research program — a closed-loop framework
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> For reference: Claude Mythos Preview leads the public leaderboard
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> at 83.1% pass@1 (overall, closed model).
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> Mythoseek is a 10B open-weight alternative.
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