Instructions to use OpenGVLab/InternVL-Chat-V1-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenGVLab/InternVL-Chat-V1-2 with Transformers:

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

pipe = pipeline("image-text-to-text", model="OpenGVLab/InternVL-Chat-V1-2", 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-V1-2", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use OpenGVLab/InternVL-Chat-V1-2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenGVLab/InternVL-Chat-V1-2"
# 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-V1-2",
		"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-V1-2

SGLang

How to use OpenGVLab/InternVL-Chat-V1-2 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-V1-2" \
    --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-V1-2",
		"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-V1-2" \
        --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-V1-2",
		"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-V1-2 with Docker Model Runner:
```
docker model run hf.co/OpenGVLab/InternVL-Chat-V1-2
```

czczup commited on Feb 13, 2024

Commit

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@@ -41,16 +41,16 @@ For more details about data preparation, please see [here](./internvl_chat#prepa
 \* Proprietary Model
-| name               | image size | MMMU<br>(val) | MMMU<br>(test) | MathVista<br>(testmini) | MMB<br>(test) | MMB−CN<br>(test) | MMVP | MME      | ScienceQA<br>(image) | POPE | SEEDv1<br>(image) | TextVQA | VizWiz | GQA  |
-| ------------------ | ---------- | ------------- | -------------- | ----------------------- | ------------- | ---------------- | ---- | -------- | -------------------- | ---- | ----------------- | ------- | ------ | ---- |
-| GPT-4V\*           | unknown    | 56.8          | 55.7           | 49.9                    | 77.0          | 74.4             | 38.7 | 1409/517 | -                    | -    | 71.6              | 78.0    | -      | -    |
-| Gemini Ultra\*     | unknown    | 59.4          | -              | 53.0                    | -             | -                | -    | -        | -                    | -    | -                 | 82.3    | -      | -    |
-| Gemini Pro\*       | unknown    | 47.9          | -              | 45.2                    | 73.6          | 74.3             | 40.7 | 1497/437 | -                    | -    | 70.7              | 74.6    | -      | -    |
-| Qwen-VL-Plus\*     | unknown    | 45.2          | 40.8           | 43.3                    | 67.0          | 70.7             | -    | 1681/502 | -                    | -    | 65.7              | 78.9    | -      | -    |
-| Qwen-VL-Max\*      | unknown    | 51.4          | 46.8            | 51.0                    | 77.6         | 75.7             | -    | -        | -                    | -    | -                 | 79.5    | -      | -    |
-|                    |            |               |                |                         |               |                  |      |          |                      |      |                   |         |        |      |
-| LLaVA-NEXT-34B     | 672x672    | 51.1          | 44.7           | 46.5                    | 79.3          | 79.0             | -    | 1631/397 | 81.8                 | 87.7 | 75.9              | 69.5    | 63.8   | 67.1 |
-| InternVL-Chat-V1.2 | 448x448    | 51.6          | 46.2           | 47.7                    | 82.2          | 81.2             | 56.7 | 1672/509 | 83.3                 | 88.0 | TODO              | 69.7    | 60.0   | 64.0 |
 - MMBench results are collected from the [leaderboard](https://mmbench.opencompass.org.cn/leaderboard).
 - In most benchmarks, InternVL-Chat-V1.2 achieves better performance than LLaVA-NeXT-34B.

 \* Proprietary Model
+| name               | image size | MMMU<br>(val) | MMMU<br>(test) | MathVista<br>(testmini) | MMB<br>(test) | MMB−CN<br>(test) | MMVP | MME      | ScienceQA<br>(image) | POPE | TextVQA | SEEDv1<br>(image) | VizWiz | GQA  |
+| ------------------ | ---------- | ------------- | -------------- | ----------------------- | ------------- | ---------------- | ---- | -------- | -------------------- | ---- | ------- | ----------------- | ------ | ---- |
+| GPT-4V\*           | unknown    | 56.8          | 55.7           | 49.9                    | 77.0          | 74.4             | 38.7 | 1409/517 | -                    | -    | 78.0    | 71.6              | -      | -    |
+| Gemini Ultra\*     | unknown    | 59.4          | -              | 53.0                    | -             | -                | -    | -        | -                    | -    | 82.3    | -                 | -      | -    |
+| Gemini Pro\*       | unknown    | 47.9          | -              | 45.2                    | 73.6          | 74.3             | 40.7 | 1497/437 | -                    | -    | 74.6    | 70.7              | -      | -    |
+| Qwen-VL-Plus\*     | unknown    | 45.2          | 40.8           | 43.3                    | 67.0          | 70.7             | -    | 1681/502 | -                    | -    | 78.9    | 65.7              | -      | -    |
+| Qwen-VL-Max\*      | unknown    | 51.4          | 46.8            | 51.0                    | 77.6         | 75.7             | -    | -        | -                    | -    | 79.5    | -                 | -      | -    |
+|                    |            |               |                |                         |               |                  |      |          |                      |      |         |                   |        |      |
+| LLaVA-NEXT-34B     | 672x672    | 51.1          | 44.7           | 46.5                    | 79.3          | 79.0             | -    | 1631/397 | 81.8                 | 87.7 | 69.5    | 75.9              | 63.8   | 67.1 |
+| InternVL-Chat-V1.2 | 448x448    | 51.6          | 46.2           | 47.7                    | 82.2          | 81.2             | 56.7 | 1672/509 | 83.3                 | 88.0 | 69.7    | 75.6              | 60.0   | 64.0 |
 - MMBench results are collected from the [leaderboard](https://mmbench.opencompass.org.cn/leaderboard).
 - In most benchmarks, InternVL-Chat-V1.2 achieves better performance than LLaVA-NeXT-34B.