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
```

squ11z1 commited on 3 days ago

Commit

15edc33

verified ·

1 Parent(s): 9ab228b

Update README.md

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Files changed (1) hide show

README.md +3 -7

README.md CHANGED Viewed

@@ -21,9 +21,8 @@ tags:
 - aer
 - merlin-research
 - qwen3_5
-base_model: deepseek-ai/DeepSeek-V4-Pro-Qwen3.5-9B-Distilled
 base_model_relation: finetune
-pipeline_tag: text-generation
 ---
 # Mythoseek
@@ -39,7 +38,7 @@ pipeline_tag: text-generation
 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 confidential enterprise pentest reports and frontier
 model distillation traces, it brings closed-source cyber AI capability
 to the open community.
@@ -118,7 +117,4 @@ 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).
-This makes Mythoseek the first cybersecurity LLM with
-quantum-informed entropy regularization.

 - aer
 - merlin-research
 - qwen3_5
 base_model_relation: finetune
+pipeline_tag: image-text-to-text
 ---
 # Mythoseek
 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.
 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).