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, device_map="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
```

czczup commited on Apr 20, 2024

Commit

a86d552

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1 Parent(s): 1f98fd5

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README.md CHANGED Viewed

@@ -12,38 +12,42 @@ pipeline_tag: visual-question-answering
 # Model Card for InternVL-Chat-V1.5
-\[[Paper](https://arxiv.org/abs/2312.14238)\]  \[[GitHub](https://github.com/OpenGVLab/InternVL)\] \[[Chat Demo](https://internvl.opengvlab.com/)\]
 ## Model Details
-- **Model Type:** vision large language model, multimodal chatbot
 - **Model Stats:**
   - Architecture: [InternViT-6B-448px-V1-5](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-5) + MLP + [InternLM2-Chat-20B](https://huggingface.co/internlm/internlm2-chat-20b)
   - Params: 25.5B
-  - Image size: dynamic resolution, max to 40 tiles of 448 x 448 during inference.
-  - Number of visual tokens: 256 * (number of tiles + 1)
 - **Training Strategy:**
   - Pretraining Stage
     - Learnable Component: ViT + MLP
-    - Data: TODO
   - SFT Stage
     - Learnable Component: ViT + MLP + LLM
-    - Data: TODO
 ## Model Usage
-We provide a minimum code example to run InternVL-Chat using only the `transformers` library.
 You also can use our [online demo](https://internvl.opengvlab.com/) for a quick experience of this model.
-Note: If you meet this error `ImportError: This modeling file requires the following packages that were not found in your environment: fastchat`, please run `pip install fschat`.
 ```python
 import json
 import os
-from internvl.model.internvl_chat import InternVLChatModel
 from transformers import AutoTokenizer, AutoModel
 from tqdm import tqdm
 import torch

 # Model Card for InternVL-Chat-V1.5
+<img src="https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/AjPIKaxKLZCbzQRrPELPB.webp" alt="Image Description" width="300" height="300">
+\[[Paper](https://arxiv.org/abs/2312.14238)\]  \[[GitHub](https://github.com/OpenGVLab/InternVL)\] \[[Chat Demo](https://internvl.opengvlab.com/)\] \[[中文解读](https://zhuanlan.zhihu.com/p/675877376)]
+| Model                   | Date       | Download                                                                    | Note                               |
+| ----------------------- | ---------- | --------------------------------------------------------------------------- | ---------------------------------- |
+| InternVL-Chat-V1.5      | 2024.04.18 | 🤗 [HF link](https://huggingface.co/OpenGVLab/InternVL-Chat-V1-5)            | support 4K image; super strong OCR; Approaching the performance of GPT-4V and Gemini Pro on various benchmarks like MMMU, DocVQA, ChartQA, MathVista, etc. (🔥new)|
+| InternVL-Chat-V1.2-Plus | 2024.02.21 | 🤗 [HF link](https://huggingface.co/OpenGVLab/InternVL-Chat-V1-2-Plus)       | more SFT data and stronger  |
+| InternVL-Chat-V1.2      | 2024.02.11 | 🤗 [HF link](https://huggingface.co/OpenGVLab/InternVL-Chat-V1-2)            | scaling up LLM to 34B       |
+| InternVL-Chat-V1.1      | 2024.01.24 | 🤗 [HF link](https://huggingface.co/OpenGVLab/InternVL-Chat-V1-1)            | support Chinese and stronger OCR   |
 ## Model Details
+- **Model Type:** multimodal large language model (MLLM)
 - **Model Stats:**
   - Architecture: [InternViT-6B-448px-V1-5](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-5) + MLP + [InternLM2-Chat-20B](https://huggingface.co/internlm/internlm2-chat-20b)
+  - Image size: dynamic resolution, max to 32 tiles of 448 x 448 (4K resolution) during inference.
   - Params: 25.5B
 - **Training Strategy:**
   - Pretraining Stage
     - Learnable Component: ViT + MLP
+    - Data: Please see our technical report.
   - SFT Stage
     - Learnable Component: ViT + MLP + LLM
+    - Data: Please see our technical report.
 ## Model Usage
+We provide an example code to run InternVL-Chat-V1.2 using `transformers`.
 You also can use our [online demo](https://internvl.opengvlab.com/) for a quick experience of this model.
 ```python
 import json
 import os
 from transformers import AutoTokenizer, AutoModel
 from tqdm import tqdm
 import torch