Instructions to use OpenGVLab/InternVL3-8B-hf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenGVLab/InternVL3-8B-hf with Transformers:

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

pipe = pipeline("image-text-to-text", model="OpenGVLab/InternVL3-8B-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/InternVL3-8B-hf")
model = AutoModelForImageTextToText.from_pretrained("OpenGVLab/InternVL3-8B-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/InternVL3-8B-hf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenGVLab/InternVL3-8B-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/InternVL3-8B-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/InternVL3-8B-hf

SGLang

How to use OpenGVLab/InternVL3-8B-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/InternVL3-8B-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/InternVL3-8B-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/InternVL3-8B-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/InternVL3-8B-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/InternVL3-8B-hf with Docker Model Runner:
```
docker model run hf.co/OpenGVLab/InternVL3-8B-hf
```

NotImplementedError: Cannot copy out of meta tensor

by obushy - opened Apr 27, 2025

Discussion

obushy

Apr 27, 2025

NotImplementedError: Cannot copy out of meta tensor; no data! Please use torch.nn.Module.to_empty() instead of torch.nn.Module.to() when moving module from meta to a different device.

yonigozlan

OpenGVLab org Apr 28, 2025

Could you provide a snippet to reproduce this?

obushy

Apr 29, 2025

•

edited Apr 29, 2025

Alright, here's the code I ran and the error message I got.

code

import os
from transformers import AutoProcessor, AutoModelForImageTextToText
import torch

torch_device = "cuda"
model_checkpoint = "../../models/OpenGVLab/InternVL3-8B-hf"
processor = AutoProcessor.from_pretrained(model_checkpoint)
model = AutoModelForImageTextToText.from_pretrained(model_checkpoint, device_map=torch_device, torch_dtype=torch.bfloat16)

messages = [
{
"role": "user",
"content": [
{"type": "image", "url": "http://images.cocodataset.org/val2017/000000039769.jpg"},
{"type": "text", "text": "Please describe the image explicitly."},
],
}
]

inputs = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt").to(model.device, dtype=torch.bfloat16)

generate_ids = model.generate(**inputs, max_new_tokens=50)
decoded_output = processor.decode(generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True)

print(decoded_output)

error

Initially, my transformers library version was 4.51.3. When I ran the code, I encountered the following error:

ValueError: The checkpoint you are trying to load has model type internvl but Transformers does not recognize this architecture. This could be because of an issue with the checkpoint, or because your version of Transformers is out of date.

You can update Transformers with the command pip install --upgrade transformers. If this does not work, and the checkpoint is very new, then there may not be a release version that supports this model yet. In this case, you can get the most up-to-date code by installing Transformers from source with the command pip install git+https://github.com/huggingface/transformers.git

After running pip install git+https://github.com/huggingface/transformers.git, it installed transformers-4.52.0.dev0. However, when I ran the code again, I encountered the following error:

Loading checkpoint shards: 100%|██████████████████████████████████████████| 4/4 [00:30<00:00, 7.55s/it]
Traceback (most recent call last):
File "/data/qa_text/VQA/Code/v7-intern3/test.py", line 9, in
model = AutoModelForImageTextToText.from_pretrained(model_checkpoint, device_map=torch_device, torch_dtype=torch.bfloat16)
File "/data/qa_text/anaconda3/envs/physics/lib/python3.9/site-packages/transformers/models/auto/auto_factory.py", line 571, in from_pretrained
return model_class.from_pretrained(
File "/data/qa_text/anaconda3/envs/physics/lib/python3.9/site-packages/transformers/modeling_utils.py", line 280, in _wrapper
return func(*args, **kwargs)
File "/data/qa_text/anaconda3/envs/physics/lib/python3.9/site-packages/transformers/modeling_utils.py", line 4592, in from_pretrained
dispatch_model(model, **device_map_kwargs)
File "/data/qa_text/anaconda3/envs/physics/lib/python3.9/site-packages/accelerate/big_modeling.py", line 501, in dispatch_model
model.to(device)
File "/data/qa_text/anaconda3/envs/physics/lib/python3.9/site-packages/transformers/modeling_utils.py", line 3767, in to
return super().to(*args, **kwargs)
File "/data/qa_text/anaconda3/envs/physics/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1174, in to
return self._apply(convert)
File "/data/qa_text/anaconda3/envs/physics/lib/python3.9/site-packages/torch/nn/modules/module.py", line 780, in _apply
module._apply(fn)
File "/data/qa_text/anaconda3/envs/physics/lib/python3.9/site-packages/torch/nn/modules/module.py", line 780, in _apply
module._apply(fn)
File "/data/qa_text/anaconda3/envs/physics/lib/python3.9/site-packages/torch/nn/modules/module.py", line 780, in _apply
module._apply(fn)
[Previous line repeated 3 more times]
File "/data/qa_text/anaconda3/envs/physics/lib/python3.9/site-packages/torch/nn/modules/module.py", line 805, in _apply
param_applied = fn(param)
File "/data/qa_text/anaconda3/envs/physics/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1167, in convert
raise NotImplementedError(
NotImplementedError: Cannot copy out of meta tensor; no data! Please use torch.nn.Module.to_empty() instead of torch.nn.Module.to() when moving module from meta to a different device.

sachmatkris

Sep 8, 2025

i am getting the same

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