Instructions to use LEONW24/BEPA-7B-S2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LEONW24/BEPA-7B-S2 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="LEONW24/BEPA-7B-S2")
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("LEONW24/BEPA-7B-S2")
model = AutoModelForImageTextToText.from_pretrained("LEONW24/BEPA-7B-S2")
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 LEONW24/BEPA-7B-S2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LEONW24/BEPA-7B-S2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LEONW24/BEPA-7B-S2",
		"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/LEONW24/BEPA-7B-S2

SGLang

How to use LEONW24/BEPA-7B-S2 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 "LEONW24/BEPA-7B-S2" \
    --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": "LEONW24/BEPA-7B-S2",
		"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 "LEONW24/BEPA-7B-S2" \
        --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": "LEONW24/BEPA-7B-S2",
		"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 LEONW24/BEPA-7B-S2 with Docker Model Runner:
```
docker model run hf.co/LEONW24/BEPA-7B-S2
```

Add model card and metadata

by nielsr HF Staff - opened Jan 12

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+50

-0

nielsr

Jan 12

Hi! I'm Niels, part of the community science team at Hugging Face. I'm opening this PR to add a model card for this repository.

A model card is essential for documenting your artifact and making it discoverable on the Hugging Face Hub. This PR includes:

Relevant YAML metadata (license, library_name, and pipeline_tag).
Links to the research paper, project page, and GitHub repository.
A concise summary of the BEPA framework and results.
BibTeX citation information.

Please let me know if you'd like to adjust any of the information!

Add model card and metadata9d8b1503

LEONW24

Owner Jan 12

Hi Niels,

Thanks for the heads-up and for your help! 🙌 I've just updated the model card with the relevant YAML metadata, research links, and citation info as you suggested. Feel free to check it out! Let me know if anything else is needed.

LEONW24 changed pull request status to closed Jan 12

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