Instructions to use OpenGVLab/InternVL3-14B-hf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenGVLab/InternVL3-14B-hf with Transformers:

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

pipe = pipeline("image-text-to-text", model="OpenGVLab/InternVL3-14B-hf")
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("OpenGVLab/InternVL3-14B-hf")
model = AutoModelForImageTextToText.from_pretrained("OpenGVLab/InternVL3-14B-hf")
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

vLLM

How to use OpenGVLab/InternVL3-14B-hf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenGVLab/InternVL3-14B-hf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenGVLab/InternVL3-14B-hf",
		"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/OpenGVLab/InternVL3-14B-hf

SGLang

How to use OpenGVLab/InternVL3-14B-hf 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 "OpenGVLab/InternVL3-14B-hf" \
    --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": "OpenGVLab/InternVL3-14B-hf",
		"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 "OpenGVLab/InternVL3-14B-hf" \
        --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": "OpenGVLab/InternVL3-14B-hf",
		"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 OpenGVLab/InternVL3-14B-hf with Docker Model Runner:
```
docker model run hf.co/OpenGVLab/InternVL3-14B-hf
```

InternVL3-14B-hf / config.json

yonigozlan HF Staff

Update config.json

1109e33 verified about 1 year ago

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	{
	"architectures": [
	"InternVLForConditionalGeneration"
	],
	"downsample_ratio": 0.5,
	"image_seq_length": 256,
	"image_token_id": 151667,
	"model_type": "internvl",
	"projector_hidden_act": "gelu",
	"text_config": {
	"architectures": [
	"Qwen2ForCausalLM"
	],
	"attention_dropout": 0.0,
	"bos_token_id": 151643,
	"eos_token_id": 151645,
	"hidden_act": "silu",
	"hidden_size": 5120,
	"initializer_range": 0.02,
	"intermediate_size": 13824,
	"max_position_embeddings": 32768,
	"max_window_layers": 70,
	"model_type": "qwen2",
	"num_attention_heads": 40,
	"num_hidden_layers": 48,
	"num_key_value_heads": 8,
	"rms_norm_eps": 1e-06,
	"rope_scaling": {
	"factor": 2.0,
	"rope_type": "dynamic",
	"type": "dynamic"
	},
	"rope_theta": 1000000.0,
	"sliding_window": null,
	"torch_dtype": "bfloat16",
	"use_cache": true,
	"use_sliding_window": false,
	"vocab_size": 151674
	},
	"torch_dtype": "bfloat16",
	"transformers_version": "4.52.0.dev0",
	"vision_config": {
	"architectures": [
	"InternVisionModel"
	],
	"attention_bias": true,
	"attention_dropout": 0.0,
	"dropout": 0.0,
	"hidden_act": "gelu",
	"hidden_dropout_prob": 0.0,
	"hidden_size": 1024,
	"image_size": [
	448,
	448
	],
	"initializer_factor": 0.1,
	"initializer_range": 1e-10,
	"intermediate_size": 4096,
	"layer_norm_eps": 1e-06,
	"layer_scale_init_value": 0.1,
	"model_type": "internvl_vision",
	"norm_type": "layer_norm",
	"num_attention_heads": 16,
	"num_channels": 3,
	"num_hidden_layers": 24,
	"patch_size": [
	14,
	14
	],
	"projection_dropout": 0.0,
	"torch_dtype": "bfloat16",
	"use_absolute_position_embeddings": true,
	"use_mask_token": false,
	"use_mean_pooling": true,
	"use_qk_norm": false
	},
	"vision_feature_layer": -1,
	"vision_feature_select_strategy": "default"
	}