Instructions to use Jinghao-Guo/LLaVA-OneVision-1.5-8B-Instruct-converted with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Jinghao-Guo/LLaVA-OneVision-1.5-8B-Instruct-converted with Transformers:

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

pipe = pipeline("image-text-to-text", model="Jinghao-Guo/LLaVA-OneVision-1.5-8B-Instruct-converted")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForSeq2SeqLM
model = AutoModelForSeq2SeqLM.from_pretrained("Jinghao-Guo/LLaVA-OneVision-1.5-8B-Instruct-converted", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Jinghao-Guo/LLaVA-OneVision-1.5-8B-Instruct-converted with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Jinghao-Guo/LLaVA-OneVision-1.5-8B-Instruct-converted"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Jinghao-Guo/LLaVA-OneVision-1.5-8B-Instruct-converted",
		"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/Jinghao-Guo/LLaVA-OneVision-1.5-8B-Instruct-converted

SGLang

How to use Jinghao-Guo/LLaVA-OneVision-1.5-8B-Instruct-converted 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 "Jinghao-Guo/LLaVA-OneVision-1.5-8B-Instruct-converted" \
    --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": "Jinghao-Guo/LLaVA-OneVision-1.5-8B-Instruct-converted",
		"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 "Jinghao-Guo/LLaVA-OneVision-1.5-8B-Instruct-converted" \
        --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": "Jinghao-Guo/LLaVA-OneVision-1.5-8B-Instruct-converted",
		"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 Jinghao-Guo/LLaVA-OneVision-1.5-8B-Instruct-converted with Docker Model Runner:
```
docker model run hf.co/Jinghao-Guo/LLaVA-OneVision-1.5-8B-Instruct-converted
```

LLaVA-OneVision-1.5-8B-Instruct-converted / inference.py

Jinghao-Guo

Upload folder using huggingface_hub

313ac66 verified 11 months ago

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from transformers import AutoTokenizer, AutoProcessor, AutoModelForCausalLM
from qwen_vl_utils import process_vision_info
model_path = "lmms-lab/LLaVA-One-Vision-1.5-8B-Instruct"

# default: Load the model on the available device(s)
model = AutoModelForCausalLM.from_pretrained(
    model_path, torch_dtype="auto", device_map="auto", trust_remote_code=True
)

# default processer
processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
            },
            {"type": "text", "text": "Describe this image."},
        ],
    }
]

# Preparation for inference
text = processor.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
    text=[text],
    images=image_inputs,
    videos=video_inputs,
    padding=True,
    return_tensors="pt",
)
inputs = inputs.to("cuda")

# Inference: Generation of the output
generated_ids = model.generate(**inputs, max_new_tokens=1024)
generated_ids_trimmed = [
    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)

	from transformers import AutoTokenizer, AutoProcessor, AutoModelForCausalLM
	from qwen_vl_utils import process_vision_info
	model_path = "lmms-lab/LLaVA-One-Vision-1.5-8B-Instruct"

	# default: Load the model on the available device(s)
	model = AutoModelForCausalLM.from_pretrained(
	model_path, torch_dtype="auto", device_map="auto", trust_remote_code=True
	)

	# default processer
	processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)

	messages = [
	{
	"role": "user",
	"content": [
	{
	"type": "image",
	"image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
	},
	{"type": "text", "text": "Describe this image."},
	],
	}
	]

	# Preparation for inference
	text = processor.apply_chat_template(
	messages, tokenize=False, add_generation_prompt=True
	)
	image_inputs, video_inputs = process_vision_info(messages)
	inputs = processor(
	text=[text],
	images=image_inputs,
	videos=video_inputs,
	padding=True,
	return_tensors="pt",
	)
	inputs = inputs.to("cuda")

	# Inference: Generation of the output
	generated_ids = model.generate(**inputs, max_new_tokens=1024)
	generated_ids_trimmed = [
	out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
	]
	output_text = processor.batch_decode(
	generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
	)
	print(output_text)