Instructions to use OpenGVLab/InternVL3_5-241B-A28B-HF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenGVLab/InternVL3_5-241B-A28B-HF with Transformers:

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

pipe = pipeline("image-text-to-text", model="OpenGVLab/InternVL3_5-241B-A28B-HF", trust_remote_code=True)
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_5-241B-A28B-HF", trust_remote_code=True)
model = AutoModelForImageTextToText.from_pretrained("OpenGVLab/InternVL3_5-241B-A28B-HF", trust_remote_code=True)
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_5-241B-A28B-HF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenGVLab/InternVL3_5-241B-A28B-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_5-241B-A28B-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_5-241B-A28B-HF

SGLang

How to use OpenGVLab/InternVL3_5-241B-A28B-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_5-241B-A28B-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_5-241B-A28B-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_5-241B-A28B-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_5-241B-A28B-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_5-241B-A28B-HF with Docker Model Runner:
```
docker model run hf.co/OpenGVLab/InternVL3_5-241B-A28B-HF
```

Weiyun1025 commited on Sep 8, 2025

Commit

b941ed6

verified ·

1 Parent(s): 0bcb984

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README.md +6 -6

README.md CHANGED Viewed

@@ -365,9 +365,9 @@ We provide an example code to run `InternVL3.5-8B-HF` using `transformers`. Plea
 ```python
 import torch
-from transformers import AutoTokenizer, AutoModel
 path = "OpenGVLab/InternVL3_5-8B-HF"
-model = AutoModel.from_pretrained(
     path,
     torch_dtype=torch.bfloat16,
     low_cpu_mem_usage=True,
@@ -379,9 +379,9 @@ model = AutoModel.from_pretrained(
 ```python
 import torch
-from transformers import AutoTokenizer, AutoModel
 path = "OpenGVLab/InternVL3_5-8B-HF"
-model = AutoModel.from_pretrained(
     path,
     torch_dtype=torch.bfloat16,
     load_in_8bit=True,
@@ -395,10 +395,10 @@ model = AutoModel.from_pretrained(
 ```python
 import math
 import torch
-from transformers import AutoTokenizer, AutoModel
 path = "OpenGVLab/InternVL3_5-8B-HF"
-model = AutoModel.from_pretrained(
     path,
     torch_dtype=torch.bfloat16,
     low_cpu_mem_usage=True,

 ```python
 import torch
+from transformers import AutoTokenizer, AutoModelForImageTextToText
 path = "OpenGVLab/InternVL3_5-8B-HF"
+model = AutoModelForImageTextToText.from_pretrained(
     path,
     torch_dtype=torch.bfloat16,
     low_cpu_mem_usage=True,
 ```python
 import torch
+from transformers import AutoTokenizer, AutoModelForImageTextToText
 path = "OpenGVLab/InternVL3_5-8B-HF"
+model = AutoModelForImageTextToText.from_pretrained(
     path,
     torch_dtype=torch.bfloat16,
     load_in_8bit=True,
 ```python
 import math
 import torch
+from transformers import AutoTokenizer, AutoModelForImageTextToText
 path = "OpenGVLab/InternVL3_5-8B-HF"
+model = AutoModelForImageTextToText.from_pretrained(
     path,
     torch_dtype=torch.bfloat16,
     low_cpu_mem_usage=True,