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
```

Love your model. MLX verison?

#16

by robertweintraub84 - opened Feb 18

Discussion

robertweintraub84

Feb 18

Love your model, works great with a plugin for Lightroom. Was wondering if there is a way to get a proper vision-enabled MLX model so I can use my Apple Neural Engines and get even better performance? Or maybe the required files to build it myself from your model? Just converting the GGUF and MMPROJ aren't enough to make it work properly in LM Studio with vision.

EricRollei

Apr 30

what plugin are you using for lightroom?

robertweintraub84

May 9

https://lrgenius.com/

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