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, AutoModelForImageTextToText

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

how to caption bulk images at onece??

#20

by 9r4n4y - opened Apr 23

Discussion

9r4n4y

Apr 23

How can i caption 10 to 50 images at once instead of one by one

XLRA

2 days ago

How can i caption 10 to 50 images at once instead of one by one

There are 3 workflows around that come with the ComfyUI-JoyCaption node in ComfyUI. One of them is exactly what you are looking for.
Just a suggestion. If your are using captioning tools in ComfyUI, you should consider using the LM Studio Chat Interface Node in Conjunction with LM Studio, it decreases captioning time by a factor of 4 and can take much more images into 1 batch.

9r4n4y

2 days ago

Thanks

9r4n4y

2 days ago

How can i caption 10 to 50 images at once instead of one by one

There are 3 workflows around that come with the ComfyUI-JoyCaption node in ComfyUI. One of them is exactly what you are looking for.
Just a suggestion. If your are using captioning tools in ComfyUI, you should consider using the LM Studio Chat Interface Node in Conjunction with LM Studio, it decreases captioning time by a factor of 4 and can take much more images into 1 batch.

9r4n4y changed discussion status to closed 2 days ago

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