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
```

"text_config" missiong for 8B model

by ce-amtic - opened Aug 29, 2025

Discussion

ce-amtic

Aug 29, 2025

I encountered this error when load the 8B model with lmdeploy v0.9.2.post1.

  File "utils/internvl_utils.py", line 60, in batch_generate_with_lmd
    _cached_lmdeploy_pipeline = pipeline(
  File ".env/miniconda3/envs/lmdeploy/lib/python3.10/site-packages/lmdeploy/api.py", line 83, in pipeline
    return pipeline_class(model_path,
  File ".env/miniconda3/envs/lmdeploy/lib/python3.10/site-packages/lmdeploy/serve/vl_async_engine.py", line 32, in __init__
    super().__init__(model_path, backend=backend, backend_config=backend_config, **kwargs)
  File ".env/miniconda3/envs/lmdeploy/lib/python3.10/site-packages/lmdeploy/serve/async_engine.py", line 284, in __init__
    self._build_turbomind(model_path=model_path, backend_config=backend_config, **kwargs)
  File ".env/miniconda3/envs/lmdeploy/lib/python3.10/site-packages/lmdeploy/serve/async_engine.py", line 338, in _build_turbomind
    self.engine = tm.TurboMind.from_pretrained(model_path,
  File ".env/miniconda3/envs/lmdeploy/lib/python3.10/site-packages/lmdeploy/turbomind/turbomind.py", line 386, in from_pretrained
    return cls(model_path=pretrained_model_name_or_path,
  File ".env/miniconda3/envs/lmdeploy/lib/python3.10/site-packages/lmdeploy/turbomind/turbomind.py", line 159, in __init__
    self._load_weights(model_source)
  File ".env/miniconda3/envs/lmdeploy/lib/python3.10/site-packages/lmdeploy/turbomind/turbomind.py", line 178, in _load_weights
    self._tm_model.export()
  File ".env/miniconda3/envs/lmdeploy/lib/python3.10/site-packages/lmdeploy/turbomind/deploy/target_model/base.py", line 210, in export
    for i, reader in self.input_model.readers():
  File ".env/miniconda3/envs/lmdeploy/lib/python3.10/site-packages/lmdeploy/turbomind/deploy/source_model/llama.py", line 113, in readers
    reader = self.Reader(param, {}, False, self.model_config, policy=self.policy)
  File ".env/miniconda3/envs/lmdeploy/lib/python3.10/site-packages/lmdeploy/turbomind/deploy/source_model/internvl.py", line 64, in __init__
    raise ValueError(f'Miss "text_config" in model config: {model_cfg}')
ValueError: Miss "text_config" in model config: None

            from lmdeploy import pipeline, TurbomindEngineConfig, ChatTemplateConfig, GenerationConfig
            backend_config = TurbomindEngineConfig(
                session_len=16384, tp=tp, dp=dp
            )
            chat_template_config = ChatTemplateConfig(
                model_name='internvl2_5'
            )
            _cached_lmdeploy_pipeline = pipeline(
                model_path,
                backend_config=backend_config,
                chat_template_config=chat_template_config,
            )

The same code works pretty well for Internvl3.5 38B model, but cannot load 8B model.

Is there any missing config files or something?

Weiyun1025

OpenGVLab org Aug 29, 2025

Please use PytorchEngineConfig instead of TurbomindEngineConfig

ce-amtic

Aug 29, 2025

Please use PytorchEngineConfig instead of TurbomindEngineConfig

Solved the problem. Thank you!

ce-amtic changed discussion status to closed Aug 29, 2025

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