Instructions to use OpenGVLab/InternVL3_5-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenGVLab/InternVL3_5-8B with Transformers:

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

pipe = pipeline("image-text-to-text", model="OpenGVLab/InternVL3_5-8B", 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 AutoModel
model = AutoModel.from_pretrained("OpenGVLab/InternVL3_5-8B", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use OpenGVLab/InternVL3_5-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenGVLab/InternVL3_5-8B"
# 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_5-8B",
		"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_5-8B

SGLang

How to use OpenGVLab/InternVL3_5-8B 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_5-8B" \
    --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_5-8B",
		"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_5-8B" \
        --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_5-8B",
		"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_5-8B with Docker Model Runner:
```
docker model run hf.co/OpenGVLab/InternVL3_5-8B
```

InternVL config to_dict() includes non-JSON-serializable fields (e.g., torch.dtype), causing PretrainedConfig.repr to crash in vLLM Ray mode

#11

by ringbird - opened Jan 14

Discussion

ringbird

Jan 14

Hi OpenGVLab team,

When deploying OpenGVLab/InternVL (e.g., InternVL2_5-8B) with vLLM (>= 0.11.1 / 0.12.x) using Ray distributed executor (--distributed-executor-backend ray), the service may fail during startup due to a config repr / JSON serialization error.

During initialization, some code path triggers repr(config) (often via structured logging / dataclass repr / Ray context). In Transformers, PretrainedConfig.__repr__() calls to_json_string() → json.dumps(config.to_dict()).

However, InternVL’s custom config to_dict() returns a dict containing non-JSON-serializable objects, e.g.:torch_dtype = torch.bfloat16 (torch.dtype)

This leads to: TypeError: Object of type dtype (or InternLM2Config) is not JSON serializable

vLLM mp mode may not trigger this as frequently, but Ray mode tends to hit repr/serialization paths more often. The core issue is that to_dict() is expected to be JSON-friendly for PretrainedConfig interoperability (repr, save/load, logging).

Please update InternVL’s config implementation (e.g., configuration_internvl_chat.py) to ensure to_dict() returns JSON-serializable types only, for example:

Convert torch.dtype to str(dtype) (e.g., "bfloat16")
Recursively convert nested PretrainedConfig objects to dicts (or otherwise ensure plain Python types)

This would improve compatibility across Transformers ecosystem tools (including vLLM Ray).

Thanks!

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InternVL config to_dict() includes non-JSON-serializable fields (e.g., torch.dtype), causing PretrainedConfig.__repr__ to crash in vLLM Ray mode

InternVL config to_dict() includes non-JSON-serializable fields (e.g., torch.dtype), causing PretrainedConfig.repr to crash in vLLM Ray mode