Instructions to use OpenGVLab/Mini-InternVL-Chat-4B-V1-5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenGVLab/Mini-InternVL-Chat-4B-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-4B-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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("OpenGVLab/Mini-InternVL-Chat-4B-V1-5", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use OpenGVLab/Mini-InternVL-Chat-4B-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-4B-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-4B-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-4B-V1-5

SGLang

How to use OpenGVLab/Mini-InternVL-Chat-4B-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-4B-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-4B-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-4B-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-4B-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-4B-V1-5 with Docker Model Runner:
```
docker model run hf.co/OpenGVLab/Mini-InternVL-Chat-4B-V1-5
```

opengvlab-admin commited on May 28, 2024

Commit

5e72178

verified ·

1 Parent(s): 95d07f1

Update README.md

Browse files

Files changed (1) hide show

README.md +5 -5

README.md CHANGED Viewed

@@ -10,7 +10,7 @@ datasets:
 pipeline_tag: visual-question-answering
 ---
-# Model Card for Mini-InternVL-Chat-2B-V1-5
 <p align="center">
   <img src="https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/D60YzQBIzvoCvLRp2gZ0A.jpeg" alt="Image Description" width="300" height="300" />
 </p>
@@ -33,12 +33,12 @@ As shown in the figure below, we adopted the same model architecture as InternVL
 ## Model Details
 - **Model Type:** multimodal large language model (MLLM)
 - **Model Stats:**
-  - Architecture: [InternViT-300M-448px](https://huggingface.co/OpenGVLab/InternViT-300M-448px) + MLP + [InternLM2-Chat-1.8B](https://huggingface.co/internlm/internlm2-chat-1_8b)
   - Image size: dynamic resolution, max to 40 tiles of 448 x 448 (4K resolution).
-  - Params: 2.2B
 - **Training Strategy:**
-  - Learnable component in the pretraining stage: ViT + MLP
   - Learnable component in the finetuning stage: ViT + MLP + LLM
   - For more details on training hyperparameters, take a look at our code: [pretrain]() | [finetune]()
@@ -57,7 +57,7 @@ As shown in the figure below, we adopted the same model architecture as InternVL
 ## Model Usage
-We provide an example code to run Mini-InternVL-Chat-2B-V1.5 using `transformers`.
 You can also use our [online demo](https://internvl.opengvlab.com/) to get a quick experience of this model.

 pipeline_tag: visual-question-answering
 ---
+# Model Card for Mini-InternVL-Chat-4B-V1-5
 <p align="center">
   <img src="https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/D60YzQBIzvoCvLRp2gZ0A.jpeg" alt="Image Description" width="300" height="300" />
 </p>
 ## Model Details
 - **Model Type:** multimodal large language model (MLLM)
 - **Model Stats:**
+  - Architecture: [InternViT-300M-448px](https://huggingface.co/OpenGVLab/InternViT-300M-448px) + MLP + [Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct)
   - Image size: dynamic resolution, max to 40 tiles of 448 x 448 (4K resolution).
+  - Params: 4.2B
 - **Training Strategy:**
+  - Learnable component in the pretraining stage: MLP
   - Learnable component in the finetuning stage: ViT + MLP + LLM
   - For more details on training hyperparameters, take a look at our code: [pretrain]() | [finetune]()
 ## Model Usage
+We provide an example code to run Mini-InternVL-Chat-4B-V1.5 using `transformers`.
 You can also use our [online demo](https://internvl.opengvlab.com/) to get a quick experience of this model.