Instructions to use fancyfeast/llama-joycaption-beta-one-hf-llava with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use fancyfeast/llama-joycaption-beta-one-hf-llava with Transformers:

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

pipe = pipeline("image-text-to-text", model="fancyfeast/llama-joycaption-beta-one-hf-llava")
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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("fancyfeast/llama-joycaption-beta-one-hf-llava")
model = AutoModelForMultimodalLM.from_pretrained("fancyfeast/llama-joycaption-beta-one-hf-llava")
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 Settings

vLLM

How to use fancyfeast/llama-joycaption-beta-one-hf-llava with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "fancyfeast/llama-joycaption-beta-one-hf-llava"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fancyfeast/llama-joycaption-beta-one-hf-llava",
		"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/fancyfeast/llama-joycaption-beta-one-hf-llava

SGLang

How to use fancyfeast/llama-joycaption-beta-one-hf-llava 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 "fancyfeast/llama-joycaption-beta-one-hf-llava" \
    --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": "fancyfeast/llama-joycaption-beta-one-hf-llava",
		"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 "fancyfeast/llama-joycaption-beta-one-hf-llava" \
        --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": "fancyfeast/llama-joycaption-beta-one-hf-llava",
		"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 fancyfeast/llama-joycaption-beta-one-hf-llava with Docker Model Runner:
```
docker model run hf.co/fancyfeast/llama-joycaption-beta-one-hf-llava
```

Needs more training on geographical locations

#13

by userx1 - opened Oct 27, 2025

Discussion

userx1

Oct 27, 2025

•

edited Oct 27, 2025

Used prompt:

Write a descriptive caption for this image in a casual tone within 30 words.

Resulted in:

New York Citys skyline at night with illuminated skyscrapers including the Twin Towers and Empire State Building against a blue-purple sky over the illuminated...

There is no "Empire State Building" here. It also falsely labeled ESB being in my similar images even though it didn't exist there.

Same thing in the spaces demo:

userx1

Oct 27, 2025

Meanwhile caption for the actual Empire State Building in the photo gives such result (no mention of ESB at all):

Photograph of a cityscape at sunset; towering skyscraper centered against a bright sky, flanked by dark buildings, with glowing orange streetlights and silhouetted cars on a busy street below.

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