Instructions to use 5CD-AI/Vintern-3B-R-beta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 5CD-AI/Vintern-3B-R-beta with Transformers:

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

pipe = pipeline("image-text-to-text", model="5CD-AI/Vintern-3B-R-beta", 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-3B-R-beta", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use 5CD-AI/Vintern-3B-R-beta with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "5CD-AI/Vintern-3B-R-beta"
# 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-3B-R-beta",
		"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-3B-R-beta

SGLang

How to use 5CD-AI/Vintern-3B-R-beta 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-3B-R-beta" \
    --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-3B-R-beta",
		"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-3B-R-beta" \
        --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-3B-R-beta",
		"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-3B-R-beta with Docker Model Runner:
```
docker model run hf.co/5CD-AI/Vintern-3B-R-beta
```

khang119966 commited on Mar 26, 2025

Commit

a4742cf

verified ·

1 Parent(s): 1f86cd1

Update README.md

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README.md +16 -10

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@@ -1,7 +1,14 @@
----
-library_name: transformers
-tags: []
----
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6336b5c831efcb5647f00170/MagGtnflTgsxkel9U71ot.png)
@@ -10,15 +17,15 @@ tags: []
 Multimodal LLM x Reasoning Model 👀 🧠 🔍
-After more than six months since creating the 5CD-AI/LLaVA-CoT-o1-Instruct dataset—one of Hugging Face’s most liked datasets of 2024 🎉—we have just completed the "base" version of the Vintern Reasoning Model!
 - This model can perform long and complex reasoning based on images, breaking down each reasoning step into multiple sub-steps while keeping hallucinations under control.
  🛠️ We also successfully implemented the GRPO algorithm on the Vintern Multimodel model!
  - Despite the difficulty of balancing multiple tasks alongside reasoning, Vintern-3B-R-beta has outperformed all previous versions across various benchmarks!
-When should you choose Vintern-1B-3.5 vs. Vintern-3B-R-beta? 🤔
-- **Vintern-1B-v3.5**: Fast ⚡ and good for Vietnamese OCR with simple text formatting. 📝
-- **Vintern-3B-R-beta**: Better for complex questions and structured text. 🔍📚 OCR performance on blurred or unclear text may be slightly affected due to our training focus on reasoning. 🔍🤖
 🚀 The next step? Training and enhancing its reasoning ability by Reinforcement Learning!
@@ -286,5 +293,4 @@ print(f'User: {question}\nAssistant: {response}')
 ## Reference
-[1] Z. Chen et al., ‘Expanding performance boundaries of open-source multimodal models with model, data, and test-time scaling’, arXiv preprint arXiv:2412. 05271, 2024.

+---
+library_name: transformers
+license: mit
+datasets:
+- 5CD-AI/LLaVA-CoT-o1-Instruct
+language:
+- vi
+- en
+- zh
+pipeline_tag: image-text-to-text
+---
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6336b5c831efcb5647f00170/MagGtnflTgsxkel9U71ot.png)
 Multimodal LLM x Reasoning Model 👀 🧠 🔍
+After more than six months since creating the [5CD-AI/LLaVA-CoT-o1-Instruct](https://huggingface.co/datasets/5CD-AI/LLaVA-CoT-o1-Instruct) dataset—one of Hugging Face’s most liked datasets of 2024 🎉—we have just completed the "base" version of the Vintern Reasoning Model!
 - This model can perform long and complex reasoning based on images, breaking down each reasoning step into multiple sub-steps while keeping hallucinations under control.
  🛠️ We also successfully implemented the GRPO algorithm on the Vintern Multimodel model!
  - Despite the difficulty of balancing multiple tasks alongside reasoning, Vintern-3B-R-beta has outperformed all previous versions across various benchmarks!
+When should you choose [Vintern-1B-v3_5](https://huggingface.co/5CD-AI/Vintern-1B-v3_5) vs Vintern-3B-R-beta? 🤔
+- **Vintern-1B-v3_5**: Fast ⚡ and good for Vietnamese OCR with simple text formatting. 📝 Highly reliable. ✅
+- **Vintern-3B-R-beta**: Better for complex questions and complex structured doc image. 🔍📚 OCR performance on blurred or unclear text may be slightly affected due to our training focus on reasoning. 🔍🤖
 🚀 The next step? Training and enhancing its reasoning ability by Reinforcement Learning!
 ## Reference
+[1] Z. Chen et al., ‘Expanding performance boundaries of open-source multimodal models with model, data, and test-time scaling’, arXiv preprint arXiv:2412. 05271, 2024.