Instructions to use OpenGVLab/Mini-InternVL-Chat-2B-V1-5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenGVLab/Mini-InternVL-Chat-2B-V1-5 with Transformers:

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

pipe = pipeline("image-text-to-text", model="OpenGVLab/Mini-InternVL-Chat-2B-V1-5", 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/Mini-InternVL-Chat-2B-V1-5", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use OpenGVLab/Mini-InternVL-Chat-2B-V1-5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenGVLab/Mini-InternVL-Chat-2B-V1-5"
# 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/Mini-InternVL-Chat-2B-V1-5",
		"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/Mini-InternVL-Chat-2B-V1-5

SGLang

How to use OpenGVLab/Mini-InternVL-Chat-2B-V1-5 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/Mini-InternVL-Chat-2B-V1-5" \
    --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/Mini-InternVL-Chat-2B-V1-5",
		"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/Mini-InternVL-Chat-2B-V1-5" \
        --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/Mini-InternVL-Chat-2B-V1-5",
		"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/Mini-InternVL-Chat-2B-V1-5 with Docker Model Runner:
```
docker model run hf.co/OpenGVLab/Mini-InternVL-Chat-2B-V1-5
```

czczup commited on May 26, 2024

Commit

f2f20b3

verified ·

1 Parent(s): 1a6b4f6

Update README.md

Browse files

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README.md +5 -4

README.md CHANGED Viewed

@@ -19,8 +19,13 @@ pipeline_tag: visual-question-answering
 \[[InternVL 1.5 Technical Report](https://arxiv.org/abs/2404.16821)\]  \[[CVPR Paper](https://arxiv.org/abs/2312.14238)\]  \[[GitHub](https://github.com/OpenGVLab/InternVL)\] \[[Chat Demo](https://internvl.opengvlab.com/)\] \[[中文解读](https://zhuanlan.zhihu.com/p/675877376)]
 We are delighted to introduce Mini-InternVL-Chat-2B-V1-5. In the era of large language models, many researchers have started to focus on smaller language models, such as Gemma-2B, Qwen-1.8B, and InternLM2-1.8B. Inspired by their efforts, we have distilled our vision foundation model InternViT-6B-448px-V1-5 down to 300M and used InternLM2-Chat-1.8B as our language model. This resulted in a small multimodal model with excellent performance.
 ## Model Details
 - **Model Type:** multimodal large language model (MLLM)
@@ -43,10 +48,6 @@ We are delighted to introduce Mini-InternVL-Chat-2B-V1-5. In the era of large la
 | InternVL-Chat-V1.2(🤗 [HF link](https://huggingface.co/OpenGVLab/InternVL-Chat-V1-2) )      |InternViT-6B-448px-V1-2(🤗 [HF link](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-2))     |2024.02.11       |             scaling up LLM to 34B       |
 | InternVL-Chat-V1.1(🤗 [HF link](https://huggingface.co/OpenGVLab/InternVL-Chat-V1-1))      |InternViT-6B-448px-V1-0(🤗 [HF link](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-0))    |2024.01.24         |   support Chinese and stronger OCR   |
-## Architecture
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/6579b818563044badca392fc/OM6Ejt8Q9ewSAsGZcDqH0.png)
 ## Performance
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/BbsilHS8PjwZwlc330_g4.png)

 \[[InternVL 1.5 Technical Report](https://arxiv.org/abs/2404.16821)\]  \[[CVPR Paper](https://arxiv.org/abs/2312.14238)\]  \[[GitHub](https://github.com/OpenGVLab/InternVL)\] \[[Chat Demo](https://internvl.opengvlab.com/)\] \[[中文解读](https://zhuanlan.zhihu.com/p/675877376)]
+You can run multimodal large models using a 1080Ti now.
 We are delighted to introduce Mini-InternVL-Chat-2B-V1-5. In the era of large language models, many researchers have started to focus on smaller language models, such as Gemma-2B, Qwen-1.8B, and InternLM2-1.8B. Inspired by their efforts, we have distilled our vision foundation model InternViT-6B-448px-V1-5 down to 300M and used InternLM2-Chat-1.8B as our language model. This resulted in a small multimodal model with excellent performance.
+As shown in the figure below, we adopted the same model architecture as InternVL 1.5. We simply replaced the original InternViT-6B with InternViT-300M and InternLM2-Chat-20B with InternLM2-Chat-1.8B. For training, we used the same data as InternVL 1.5 to train this smaller model. Additionally, due to the lower training costs of smaller models, we used a context length of 8K during training.
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/SmtrR0c5NVhmSTvnwwCE9.png)
 ## Model Details
 - **Model Type:** multimodal large language model (MLLM)
 | InternVL-Chat-V1.2(🤗 [HF link](https://huggingface.co/OpenGVLab/InternVL-Chat-V1-2) )      |InternViT-6B-448px-V1-2(🤗 [HF link](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-2))     |2024.02.11       |             scaling up LLM to 34B       |
 | InternVL-Chat-V1.1(🤗 [HF link](https://huggingface.co/OpenGVLab/InternVL-Chat-V1-1))      |InternViT-6B-448px-V1-0(🤗 [HF link](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-0))    |2024.01.24         |   support Chinese and stronger OCR   |
 ## Performance
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/BbsilHS8PjwZwlc330_g4.png)