Instructions to use OpenGVLab/InternVL3-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenGVLab/InternVL3-2B with Transformers:

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

pipe = pipeline("image-text-to-text", model="OpenGVLab/InternVL3-2B", 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/InternVL3-2B", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use OpenGVLab/InternVL3-2B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenGVLab/InternVL3-2B"
# 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/InternVL3-2B",
		"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/InternVL3-2B

SGLang

How to use OpenGVLab/InternVL3-2B 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/InternVL3-2B" \
    --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/InternVL3-2B",
		"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/InternVL3-2B" \
        --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/InternVL3-2B",
		"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/InternVL3-2B with Docker Model Runner:
```
docker model run hf.co/OpenGVLab/InternVL3-2B
```

Weiyun1025 commited on Apr 12, 2025

Commit

1594f77

verified ·

1 Parent(s): 0458c88

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@@ -9,7 +9,7 @@ base_model:
   - Qwen/Qwen2.5-1.5B
 base_model_relation: merge
 datasets:
-  - OpenGVLab/MMPR-v1.1
 language:
   - multilingual
 tags:
@@ -31,7 +31,7 @@ tags:
 We introduce InternVL3, an advanced multimodal large language model (MLLM) series that demonstrates superior overall performance.
 Compared to InternVL 2.5, InternVL3 exhibits superior multimodal perception and reasoning capabilities, while further extending its multimodal capabilities to encompass tool usage, GUI agents, industrial image analysis, 3D vision perception, and more.
-Additionally, benefitting from Native Multimodal Pre-Training, the InternVL3 series achieves even better overall text performance than the Qwen2.5 series, which serves as the initialization of the langauge component in InternVL3.
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/619507e7b74b6c591f794340/fMRWSzB8ysrafQ2XJW9WR.png)
@@ -49,6 +49,8 @@ In the following table, we provide an overview of the InternVL3 series.
 | InternVL3-38B |   [InternViT-6B-448px-V2_5](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V2_5)   |             [Qwen2.5-32B](https://huggingface.co/Qwen/Qwen2.5-32B)             | [🤗 link](https://huggingface.co/OpenGVLab/InternVL3-38B) |
 | InternVL3-78B |   [InternViT-6B-448px-V2_5](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V2_5)   |             [Qwen2.5-72B](https://huggingface.co/Qwen/Qwen2.5-72B)             | [🤗 link](https://huggingface.co/OpenGVLab/InternVL3-78B) |
 ## Model Architecture
 As shown in the following figure, [InternVL3](https://internvl.github.io/blog/2025-04-11-InternVL-3/) retains the same model architecture as [InternVL 2.5](https://internvl.github.io/blog/2024-12-05-InternVL-2.5/) and its predecessors, InternVL 1.5 and 2.0, following the "ViT-MLP-LLM" paradigm. In this new version, we integrate a newly incrementally pre-trained InternViT with various pre-trained LLMs, including InternLM 3 and Qwen 2.5, using a randomly initialized MLP projector.
@@ -104,35 +106,45 @@ In this work, we use the Best-of-N evaluation strategy and employ [VisualPRM-8B]
 ### Multimodal Reasoning and Mathematics
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/619507e7b74b6c591f794340/Vg18MtN8tpX-eF4ls6m26.png)
 ### OCR, Chart, and Document Understanding
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/619507e7b74b6c591f794340/cx3DG7RfFg5LvHfbjqyWg.png)
 ### Multi-Image & Real-World Comprehension
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/619507e7b74b6c591f794340/po72y3ypO6_RmtXty0b0U.png)
 ### Comprehensive Multimodal & Hallucination Evaluation
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/619507e7b74b6c591f794340/RFykBfuGY8sJ3paZpmj39.png)
 ### Visual Grounding
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/619507e7b74b6c591f794340/N2V8VPGmcivyKvOAS7F5H.png)
 ### Multimodal Multilingual Understanding
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/619507e7b74b6c591f794340/wPKL20-qJj4HZGuLn1bmf.png)
 ### Video Understanding
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/619507e7b74b6c591f794340/NuC0HDxJyW-JmvckMAqE8.png)
 ## Evaluation on Language Capability
-Benefitting from Native Multimodal Pre-Training, the InternVL3 series achieves even better overall text performance than the Qwen2.5 series, which serves as the initialization of the langauge component in InternVL3.
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/619507e7b74b6c591f794340/geqAQiiganmQiDY65VIpm.png)

   - Qwen/Qwen2.5-1.5B
 base_model_relation: merge
 datasets:
+  - OpenGVLab/MMPR-v1.2
 language:
   - multilingual
 tags:
 We introduce InternVL3, an advanced multimodal large language model (MLLM) series that demonstrates superior overall performance.
 Compared to InternVL 2.5, InternVL3 exhibits superior multimodal perception and reasoning capabilities, while further extending its multimodal capabilities to encompass tool usage, GUI agents, industrial image analysis, 3D vision perception, and more.
+Additionally, we compare InternVL3 with  Qwen2.5 Chat models, whose corresponding pre-trained base models are employed as the initialization of the langauge component in InternVL3. Benefitting from Native Multimodal Pre-Training, the InternVL3 series achieves even better overall text performance than the Qwen2.5 series.
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/619507e7b74b6c591f794340/fMRWSzB8ysrafQ2XJW9WR.png)
 | InternVL3-38B |   [InternViT-6B-448px-V2_5](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V2_5)   |             [Qwen2.5-32B](https://huggingface.co/Qwen/Qwen2.5-32B)             | [🤗 link](https://huggingface.co/OpenGVLab/InternVL3-38B) |
 | InternVL3-78B |   [InternViT-6B-448px-V2_5](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V2_5)   |             [Qwen2.5-72B](https://huggingface.co/Qwen/Qwen2.5-72B)             | [🤗 link](https://huggingface.co/OpenGVLab/InternVL3-78B) |
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/619507e7b74b6c591f794340/9wf54ERGoiM3-QICkj3Oc.png)
 ## Model Architecture
 As shown in the following figure, [InternVL3](https://internvl.github.io/blog/2025-04-11-InternVL-3/) retains the same model architecture as [InternVL 2.5](https://internvl.github.io/blog/2024-12-05-InternVL-2.5/) and its predecessors, InternVL 1.5 and 2.0, following the "ViT-MLP-LLM" paradigm. In this new version, we integrate a newly incrementally pre-trained InternViT with various pre-trained LLMs, including InternLM 3 and Qwen 2.5, using a randomly initialized MLP projector.
 ### Multimodal Reasoning and Mathematics
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/619507e7b74b6c591f794340/uVIhB9BKlirAc6zlQYI0a.png)
 ### OCR, Chart, and Document Understanding
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/619507e7b74b6c591f794340/ivWJi3Rx_snJTfq--_9kD.png)
 ### Multi-Image & Real-World Comprehension
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/619507e7b74b6c591f794340/y1OnFvJxei4dd9ZzCZ6yo.png)
 ### Comprehensive Multimodal & Hallucination Evaluation
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/619507e7b74b6c591f794340/XeBACd5_k_1lBbT70c6rp.png)
 ### Visual Grounding
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/619507e7b74b6c591f794340/LFBrfb6amBxL_WKOqs5gr.png)
 ### Multimodal Multilingual Understanding
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/619507e7b74b6c591f794340/1wc3dA3KwIeMWOVja2GF0.png)
 ### Video Understanding
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/619507e7b74b6c591f794340/qU5-6VDYtFcXxSq2bNH7T.png)
+### GUI Grounding
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/619507e7b74b6c591f794340/FRmXQGxgjbp1O_fYeJ2he.png)
+### Spatial Reasoning
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/619507e7b74b6c591f794340/J4AmWoDuJ6JwILdtsNJcN.png)
 ## Evaluation on Language Capability
+We compare InternVL3 with  Qwen2.5 Chat models, whose corresponding pre-trained base models are employed as the initialization of the langauge component in InternVL3.
+Benefitting from Native Multimodal Pre-Training, the InternVL3 series achieves even better overall text performance than the Qwen2.5 series.
+Please note that the evaluation scores of Qwen2.5 series  may differ from those officially reported, as we have adopted the prompt versions provided in the table across all datasets for OpenCompass evaluation.
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/619507e7b74b6c591f794340/geqAQiiganmQiDY65VIpm.png)