Instructions to use OpenGVLab/InternVL-Chat-V1-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenGVLab/InternVL-Chat-V1-1 with Transformers:

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

pipe = pipeline("image-text-to-text", model="OpenGVLab/InternVL-Chat-V1-1", trust_remote_code=True)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("OpenGVLab/InternVL-Chat-V1-1", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use OpenGVLab/InternVL-Chat-V1-1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenGVLab/InternVL-Chat-V1-1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenGVLab/InternVL-Chat-V1-1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/OpenGVLab/InternVL-Chat-V1-1

SGLang

How to use OpenGVLab/InternVL-Chat-V1-1 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/InternVL-Chat-V1-1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenGVLab/InternVL-Chat-V1-1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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/InternVL-Chat-V1-1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenGVLab/InternVL-Chat-V1-1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use OpenGVLab/InternVL-Chat-V1-1 with Docker Model Runner:
```
docker model run hf.co/OpenGVLab/InternVL-Chat-V1-1
```

InternVL-Chat-V1-1 / index.html

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	<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01//EN" "http://www.w3.org/TR/html4/strict.dtd">
	<html>
	<head>
	<meta http-equiv="Content-Type" content="text/html; charset=utf-8">
	<title>Directory listing for /InternVL-Chat-V1-1/</title>
	</head>
	<body>
	<h1>Directory listing for /InternVL-Chat-V1-1/</h1>
	<hr>
	<ul>
	<li><a href=".gitattributes">.gitattributes</a></li>
	<li><a href="added_tokens.json">added_tokens.json</a></li>
	<li><a href="config.json">config.json</a></li>
	<li><a href="configuration_intern_vit.py">configuration_intern_vit.py</a></li>
	<li><a href="configuration_internvl_chat.py">configuration_internvl_chat.py</a></li>
	<li><a href="conversation.py">conversation.py</a></li>
	<li><a href="modeling_intern_vit.py">modeling_intern_vit.py</a></li>
	<li><a href="modeling_internvl_chat.py">modeling_internvl_chat.py</a></li>
	<li><a href="preprocessor_config.json">preprocessor_config.json</a></li>
	<li><a href="pytorch_model-00001-of-00004.bin">pytorch_model-00001-of-00004.bin</a></li>
	<li><a href="pytorch_model-00002-of-00004.bin">pytorch_model-00002-of-00004.bin</a></li>
	<li><a href="pytorch_model-00003-of-00004.bin">pytorch_model-00003-of-00004.bin</a></li>
	<li><a href="pytorch_model-00004-of-00004.bin">pytorch_model-00004-of-00004.bin</a></li>
	<li><a href="pytorch_model.bin.index.json">pytorch_model.bin.index.json</a></li>
	<li><a href="special_tokens_map.json">special_tokens_map.json</a></li>
	<li><a href="tokenizer.model">tokenizer.model</a></li>
	<li><a href="tokenizer_config.json">tokenizer_config.json</a></li>
	</ul>
	<hr>
	</body>
	</html>