Instructions to use 5CD-AI/Vintern-1B-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 5CD-AI/Vintern-1B-v2 with Transformers:

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

pipe = pipeline("image-text-to-text", model="5CD-AI/Vintern-1B-v2", 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("5CD-AI/Vintern-1B-v2", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use 5CD-AI/Vintern-1B-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "5CD-AI/Vintern-1B-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "5CD-AI/Vintern-1B-v2",
		"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/5CD-AI/Vintern-1B-v2

SGLang

How to use 5CD-AI/Vintern-1B-v2 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 "5CD-AI/Vintern-1B-v2" \
    --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": "5CD-AI/Vintern-1B-v2",
		"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 "5CD-AI/Vintern-1B-v2" \
        --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": "5CD-AI/Vintern-1B-v2",
		"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 5CD-AI/Vintern-1B-v2 with Docker Model Runner:
```
docker model run hf.co/5CD-AI/Vintern-1B-v2
```

khang119966 commited on Nov 7, 2024

Commit

2a26621

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@@ -96,13 +96,12 @@ The benchmark result in [MTVQA](https://github.com/bytedance/MTVQA/tree/main)
 | MiniCPM-V2.5                     | ✓           | 15.3             |
 | InternVL-V1.5                    | ✗           | 12.4             |
-## OpenCompass Benchmark
 <!--
 <div align="center">
   <img src="radar_chart.png" width="400"/>
 </div> -->
-We evaluate Vintern-1B-v2 on [VLMEvalKit](https://github.com/open-compass/VLMEvalKit). (We use GPT4o-mini for some judge model)
 The current results are at a quite good level, and we are expanding the training set in English and other languages to approach models within a comparable parameter range.
@@ -125,7 +124,7 @@ The current results are at a quite good level, and we are expanding the training
 | HallBenchavg     | 38.0         | 36.1          | 41.7        | -               |
 | MathVistatestmini| 46.0         | 39.8          | 43.0        | 32.9            |
-We are still working on more detailed benchmarks.
 ## Examples

 | MiniCPM-V2.5                     | ✓           | 15.3             |
 | InternVL-V1.5                    | ✗           | 12.4             |
 <!--
 <div align="center">
   <img src="radar_chart.png" width="400"/>
 </div> -->
+<!-- We evaluate Vintern-1B-v2 on [VLMEvalKit](https://github.com/open-compass/VLMEvalKit). (We use GPT4o-mini for some judge model)
 The current results are at a quite good level, and we are expanding the training set in English and other languages to approach models within a comparable parameter range.
 | HallBenchavg     | 38.0         | 36.1          | 41.7        | -               |
 | MathVistatestmini| 46.0         | 39.8          | 43.0        | 32.9            |
+We are still working on more detailed benchmarks. -->
 ## Examples