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

Libraries

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

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

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Install from pip and serve model

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

SGLang

How to use OpenGVLab/InternVL-Chat-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/InternVL-Chat-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/InternVL-Chat-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/InternVL-Chat-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/InternVL-Chat-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/InternVL-Chat-V1-5 with Docker Model Runner:
```
docker model run hf.co/OpenGVLab/InternVL-Chat-V1-5
```

add AIBOM

#28

by sabato-nocera - opened Jun 14, 2025

base: refs/heads/main

←

from: refs/pr/28

Discussion Files changed

+77

-0

Files changed (1) hide show

OpenGVLab_InternVL-Chat-V1-5.json +77 -0

OpenGVLab_InternVL-Chat-V1-5.json ADDED Viewed

	@@ -0,0 +1,77 @@

+{
+    "bomFormat": "CycloneDX",
+    "specVersion": "1.6",
+    "serialNumber": "urn:uuid:0fd73414-9e65-47c5-8598-9745c0e73210",
+    "version": 1,
+    "metadata": {
+        "timestamp": "2025-06-05T09:41:23.012895+00:00",
+        "component": {
+            "type": "machine-learning-model",
+            "bom-ref": "OpenGVLab/InternVL-Chat-V1-5-c62ebeb8-2b6a-572f-945a-ebc349c329f4",
+            "name": "OpenGVLab/InternVL-Chat-V1-5",
+            "externalReferences": [
+                {
+                    "url": "https://huggingface.co/OpenGVLab/InternVL-Chat-V1-5",
+                    "type": "documentation"
+                }
+            ],
+            "modelCard": {
+                "modelParameters": {
+                    "task": "image-text-to-text",
+                    "architectureFamily": "internvl_chat",
+                    "modelArchitecture": "InternVLChatModel"
+                },
+                "properties": [
+                    {
+                        "name": "library_name",
+                        "value": "transformers"
+                    },
+                    {
+                        "name": "base_model",
+                        "value": "OpenGVLab/InternViT-6B-448px-V1-5, internlm/internlm2-chat-20b"
+                    },
+                    {
+                        "name": "base_model_relation",
+                        "value": "merge"
+                    }
+                ]
+            },
+            "authors": [
+                {
+                    "name": "OpenGVLab"
+                }
+            ],
+            "licenses": [
+                {
+                    "license": {
+                        "id": "MIT",
+                        "url": "https://spdx.org/licenses/MIT.html"
+                    }
+                }
+            ],
+            "description": "<p align=\"center\"><img src=\"https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/D60YzQBIzvoCvLRp2gZ0A.jpeg\" alt=\"Image Description\" width=\"300\" height=\"300\"></p>> _Two interns holding hands, symbolizing the integration of InternViT and InternLM._We introduce InternVL 1.5, an open-source multimodal large language model (MLLM) to bridge the capability gap between open-source and proprietary commercial models in multimodal understanding.We introduce three simple designs:1. **Strong Vision Encoder:** we explored a continuous learning strategy for the large-scale vision foundation model---InternViT-6B, boosting its visual understanding capabilities, and making it can be transferred and reused in different LLMs.2. **Dynamic High-Resolution:** we divide images into tiles ranging from 1 to 40 of 448 \u00d7 448 pixels according to the aspect ratio and resolution of the input images, which supports up to 4K resolution input during inference.3. **High-Quality Bilingual Dataset:** we carefully collected a high-quality bilingual dataset that covers common scenes, document images, and annotated them with English and Chinese question-answer pairs, significantly enhancing performance in OCR- and Chinese-related tasks.",
+            "tags": [
+                "transformers",
+                "tensorboard",
+                "safetensors",
+                "internvl_chat",
+                "feature-extraction",
+                "internvl",
+                "custom_code",
+                "image-text-to-text",
+                "conversational",
+                "multilingual",
+                "arxiv:2312.14238",
+                "arxiv:2404.16821",
+                "arxiv:2410.16261",
+                "arxiv:2412.05271",
+                "base_model:OpenGVLab/InternViT-6B-448px-V1-5",
+                "base_model:merge:OpenGVLab/InternViT-6B-448px-V1-5",
+                "base_model:internlm/internlm2-chat-20b",
+                "base_model:merge:internlm/internlm2-chat-20b",
+                "license:mit",
+                "region:us"
+            ]
+        }
+    }
+}