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

6c604c0

verified ·

1 Parent(s): 8a3e5e1

Add CyberGym and IFBench images to Results section

Browse files

Files changed (1) hide show

README.md +13 -1

README.md CHANGED Viewed

@@ -65,13 +65,19 @@ coefficient calibration during GRPO training.
 ---
-## CyberGym Results (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.
 | Level | Scaffold | pass@4 |
 |---|---|---|
 | Level 0 | Full scaffolding | 62% |
@@ -83,6 +89,12 @@ PoC that crashes the pre-patch build.
 > at 83.1% pass@1 (overall, closed model).
 > Mythoseek is a 10B open-weight alternative.
 ---
 ## Intended Use

 ---
+## 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% |
 > 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