Instructions to use NCSOFT/VARCO-VISION-14B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NCSOFT/VARCO-VISION-14B with Transformers:

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

pipe = pipeline("image-text-to-text", model="NCSOFT/VARCO-VISION-14B")
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 AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NCSOFT/VARCO-VISION-14B")
model = AutoModelForCausalLM.from_pretrained("NCSOFT/VARCO-VISION-14B", device_map="auto")
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 = tokenizer.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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use NCSOFT/VARCO-VISION-14B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NCSOFT/VARCO-VISION-14B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NCSOFT/VARCO-VISION-14B",
		"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/NCSOFT/VARCO-VISION-14B

SGLang

How to use NCSOFT/VARCO-VISION-14B 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 "NCSOFT/VARCO-VISION-14B" \
    --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": "NCSOFT/VARCO-VISION-14B",
		"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 "NCSOFT/VARCO-VISION-14B" \
        --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": "NCSOFT/VARCO-VISION-14B",
		"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 NCSOFT/VARCO-VISION-14B with Docker Model Runner:
```
docker model run hf.co/NCSOFT/VARCO-VISION-14B
```

VARCO-VISION-14B / tokenizer_config.json

kimyoungjune

Upload 20 files

895e8c8 verified almost 2 years ago

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	{
	"add_bos_token": false,
	"add_prefix_space": false,
	"added_tokens_decoder": {
	"151643": {
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	"151663": {
	"content": "<\|repo_name\|>",
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	},
	"151664": {
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	"151665": {
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	"content": "<gro>",
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	"special": true
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	"151667": {
	"content": "<ocr>",
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	"151668": {
	"content": "<char>",
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	}
	},
	"additional_special_tokens": [
	"<gro>",
	"<ocr>",
	"<char>",
	"</char>",
	"<obj>",
	"</obj>",
	"<bbox>",
	"</bbox>",
	"<delim>"
	],
	"bos_token": null,
	"chat_template": "{% if messages[0]['role'] != 'system' %}{{ '<\|im_start\|>system\nYou are a helpful assistant.<\|im_end\|>\n' }}{% else %}{{ '<\|im_start\|>system\n' + messages[0]['content'] + '<\|im_end\|>\n' }}{% endif %}{% for message in messages %}{{ '<\|im_start\|>' + message['role'] + '\n' }}{# Render all images first #}{% for content in message['content'] \| selectattr('type', 'equalto', 'image') %}{{ '<image>\n' }}{% endfor %}{# Render all text next #}{% if message['role'] != 'assistant' %}{% for content in message['content'] \| selectattr('type', 'equalto', 'text') %}{{ content['text'] }}{% endfor %}{% else %}{% for content in message['content'] \| selectattr('type', 'equalto', 'text') %}{% generation %}{{ content['text'] }}{% endgeneration %}{% endfor %}{% endif %}{{ '<\|im_end\|>\n' }}{% endfor %}{% if add_generation_prompt %}{{ '<\|im_start\|>assistant\n' }}{% endif %}",
	"pad_token": "[UNK]",
	"padding_side": "right",
	"split_special_tokens": false,
	"tokenizer_class": "Qwen2Tokenizer",
	"unk_token": "[UNK]"
	}