Instructions to use OpenGVLab/InternVL2-8B-MPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenGVLab/InternVL2-8B-MPO with Transformers:

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

pipe = pipeline("image-text-to-text", model="OpenGVLab/InternVL2-8B-MPO", 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/InternVL2-8B-MPO", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use OpenGVLab/InternVL2-8B-MPO with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenGVLab/InternVL2-8B-MPO"
# 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/InternVL2-8B-MPO",
		"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/InternVL2-8B-MPO

SGLang

How to use OpenGVLab/InternVL2-8B-MPO 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/InternVL2-8B-MPO" \
    --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/InternVL2-8B-MPO",
		"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/InternVL2-8B-MPO" \
        --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/InternVL2-8B-MPO",
		"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/InternVL2-8B-MPO with Docker Model Runner:
```
docker model run hf.co/OpenGVLab/InternVL2-8B-MPO
```

Weiyun1025 commited on Nov 14, 2024

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@@ -16,24 +16,20 @@ tags:
 ---
 # InternVL2-8B-MPO
-[\[📂 GitHub\]](https://github.com/OpenGVLab/InternVL)  [\[🆕 Blog\]](https://internvl.github.io/blog/2024-11-14-InternVL-2.0-MPO/)  [\[📜 Paper\]](https://internvl.github.io/blog/2024-11-14-InternVL-2.0-MPO/) [\[📖 Documents\]](https://internvl.readthedocs.io/en/latest/internvl2.0/preference_optimization.html)
 [切换至中文版](#简介)
-![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/619507e7b74b6c591f794340/ZNSqJ_rYzNUGk6rJVjEfq.jpeg)
 ## Introduction
 Existing open-source multimodal large language models (MLLMs) generally follow a training process involving pre-training and supervised fine-tuning. However, these models suffer from distribution shifts, which limit their multimodal reasoning, particularly in the Chain-of-Thought (CoT) performance.
-To address this, we introduce a preference optimization (PO) process to enhance the multimodal reasoning capabilities of MLLMs.
-Specifically,
-(1) on the data side, we design an automated preference data construction pipeline to create [MMPR](https://huggingface.co/datasets/OpenGVLab/MMPR), a high-quality, large-scale multimodal reasoning preference dataset;
-and (2) on the model side, we explore integrating PO with MLLMs, developing a simple yet effective method, termed Mixed Preference Optimization (MPO), that boosts multimodal CoT performance.
-Our approach demonstrates improved performance across multiple benchmarks, particularly in multimodal reasoning tasks.
-Notably, our model, [InternVL2-8B-MPO](https://huggingface.co/OpenGVLab/InternVL2-8B), achieves an accuracy of 67.0 on MathVista, outperforming InternVL2-8B by 8.7 points and achieving performance comparable to the 10$\times$ larger InternVL2-76B.
-We hope this study could inspire further advancements in MLLMs.
 ## Model Details

 ---
 # InternVL2-8B-MPO
+[\[📂 GitHub\]](https://github.com/OpenGVLab/InternVL/tree/main/internvl_chat/shell/internvl2.0_mpo)  [\[🆕 Blog\]](https://internvl.github.io/blog/2024-11-14-InternVL-2.0-MPO/)  [\[📜 Paper\]](https://internvl.github.io/blog/2024-11-14-InternVL-2.0-MPO/) [\[📖 Documents\]](https://internvl.readthedocs.io/en/latest/internvl2.0/preference_optimization.html)
 [切换至中文版](#简介)
+![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/619507e7b74b6c591f794340/sy8aVC1Y5wtAjG-OQzrDI.jpeg)
 ## Introduction
 Existing open-source multimodal large language models (MLLMs) generally follow a training process involving pre-training and supervised fine-tuning. However, these models suffer from distribution shifts, which limit their multimodal reasoning, particularly in the Chain-of-Thought (CoT) performance.
+To address this, we introduce a preference optimization (PO) process to enhance the multimodal reasoning capabilities of MLLMs. Specifically, (1) on the data side, we design an automated preference data construction pipeline to create [MMPR](https://huggingface.co/datasets/OpenGVLab/MMPR), a high-quality, large-scale multimodal reasoning preference dataset. and (2) on the model side, we explore integrating PO with MLLMs, developing a simple yet effective method, termed Mixed Preference Optimization (MPO), which boosts multimodal CoT performance.
+Our approach demonstrates improved performance across multiple benchmarks, particularly in multimodal reasoning tasks. Notably, our model, [InternVL2-8B-MPO](https://huggingface.co/OpenGVLab/InternVL2-8B), achieves an accuracy of 67.0 on MathVista, outperforming InternVL2-8B by 8.7 points and achieving performance comparable to the 10$\times$ larger InternVL2-76B. We hope this study could inspire further advancements in MLLMs.
 ## Model Details