Instructions to use OpenGVLab/InternVL2-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenGVLab/InternVL2-4B with Transformers:

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

pipe = pipeline("image-text-to-text", model="OpenGVLab/InternVL2-4B", 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/InternVL2-4B", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use OpenGVLab/InternVL2-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenGVLab/InternVL2-4B"
# 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/InternVL2-4B",
		"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/InternVL2-4B

SGLang

How to use OpenGVLab/InternVL2-4B 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/InternVL2-4B" \
    --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/InternVL2-4B",
		"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/InternVL2-4B" \
        --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/InternVL2-4B",
		"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/InternVL2-4B with Docker Model Runner:
```
docker model run hf.co/OpenGVLab/InternVL2-4B
```

cuierfei commited on Jul 29, 2024

Commit

9f7c091

verified ·

1 Parent(s): eedb1e9

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README.md +4 -4

README.md CHANGED Viewed

@@ -576,7 +576,7 @@ To deploy InternVL2 as an API, please configure the chat template config first.
 LMDeploy's `api_server` enables models to be easily packed into services with a single command. The provided RESTful APIs are compatible with OpenAI's interfaces. Below are an example of service startup:
 ```shell
-lmdeploy serve api_server OpenGVLab/InternVL2-4B --model-name InternVL2-4B --backend pytorch --server-port 23333 --chat-template chat_template.json
 ```
 To use the OpenAI-style interface, you need to install OpenAI:
@@ -593,7 +593,7 @@ from openai import OpenAI
 client = OpenAI(api_key='YOUR_API_KEY', base_url='http://0.0.0.0:23333/v1')
 model_name = client.models.list().data[0].id
 response = client.chat.completions.create(
-    model="InternVL2-4B",
     messages=[{
         'role':
         'user',
@@ -872,7 +872,7 @@ print(sess.response.text)
 LMDeploy 的 `api_server` 使模型能够通过一个命令轻松打包成服务。提供的 RESTful API 与 OpenAI 的接口兼容。以下是服务启动的示例：
 ```shell
-lmdeploy serve api_server OpenGVLab/InternVL2-4B --model-name InternVL2-4B --backend pytorch --server-port 23333 --chat-template chat_template.json
 ```
 为了使用OpenAI风格的API接口，您需要安装OpenAI:
@@ -889,7 +889,7 @@ from openai import OpenAI
 client = OpenAI(api_key='YOUR_API_KEY', base_url='http://0.0.0.0:23333/v1')
 model_name = client.models.list().data[0].id
 response = client.chat.completions.create(
-    model="InternVL2-4B",
     messages=[{
         'role':
         'user',

 LMDeploy's `api_server` enables models to be easily packed into services with a single command. The provided RESTful APIs are compatible with OpenAI's interfaces. Below are an example of service startup:
 ```shell
+lmdeploy serve api_server OpenGVLab/InternVL2-4B --backend pytorch --server-port 23333 --chat-template chat_template.json
 ```
 To use the OpenAI-style interface, you need to install OpenAI:
 client = OpenAI(api_key='YOUR_API_KEY', base_url='http://0.0.0.0:23333/v1')
 model_name = client.models.list().data[0].id
 response = client.chat.completions.create(
+    model=model_name,
     messages=[{
         'role':
         'user',
 LMDeploy 的 `api_server` 使模型能够通过一个命令轻松打包成服务。提供的 RESTful API 与 OpenAI 的接口兼容。以下是服务启动的示例：
 ```shell
+lmdeploy serve api_server OpenGVLab/InternVL2-4B --backend pytorch --server-port 23333 --chat-template chat_template.json
 ```
 为了使用OpenAI风格的API接口，您需要安装OpenAI:
 client = OpenAI(api_key='YOUR_API_KEY', base_url='http://0.0.0.0:23333/v1')
 model_name = client.models.list().data[0].id
 response = client.chat.completions.create(
+    model=model_name,
     messages=[{
         'role':
         'user',