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
```

czczup commited on May 29, 2024

Commit

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@@ -15,7 +15,9 @@ pipeline_tag: visual-question-answering
   <img src="https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/4IG0h_KJ2cvpp9Kdm0Jf7.webp" alt="Image Description" width="300" height="300">
 </p>
-\[[InternVL 1.5 Technical Report](https://arxiv.org/abs/2404.16821)\]  \[[Paper](https://arxiv.org/abs/2312.14238)\]  \[[GitHub](https://github.com/OpenGVLab/InternVL)\] \[[Chat Demo](https://internvl.opengvlab.com/)\] \[[中文解读](https://zhuanlan.zhihu.com/p/675877376)]
 We released InternVL-Chat-V1-1, featuring a structure similar to LLaVA, including a ViT, an MLP projector, and an LLM. In this version, we explored increasing the resolution to 448x448, enhancing OCR capabilities, and improving support for Chinese conversations.

   <img src="https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/4IG0h_KJ2cvpp9Kdm0Jf7.webp" alt="Image Description" width="300" height="300">
 </p>
+[\[🆕 Blog\]](https://internvl.github.io/blog/)  [\[📜 InternVL 1.0 Paper\]](https://arxiv.org/abs/2312.14238)  [\[📜 InternVL 1.5 Report\]](https://arxiv.org/abs/2404.16821)  [\[🗨️ Chat Demo\]](https://internvl.opengvlab.com/)
+[\[🤗 HF Demo\]](https://huggingface.co/spaces/OpenGVLab/InternVL)  [\[🚀 Quick Start\]](#model-usage)  [\[🌐 Community-hosted API\]](https://rapidapi.com/adushar1320/api/internvl-chat)  [\[📖 中文解读\]](https://zhuanlan.zhihu.com/p/675877376)
 We released InternVL-Chat-V1-1, featuring a structure similar to LLaVA, including a ViT, an MLP projector, and an LLM. In this version, we explored increasing the resolution to 448x448, enhancing OCR capabilities, and improving support for Chinese conversations.