Image-Text-to-Text
Transformers
Safetensors
Vietnamese
English
Chinese
internvl_chat
feature-extraction
conversational
custom_code
Instructions to use 5CD-AI/Vintern-3B-R-beta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
- 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
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library_name: transformers
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license: mit
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datasets:
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pipeline_tag: image-text-to-text
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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!
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- 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.
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🛠️ We also successfully implemented the GRPO algorithm on the Vintern Multimodel model!
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- Despite the difficulty of balancing multiple tasks alongside reasoning, Vintern-3B-R-beta has outperformed all previous versions across various benchmarks!
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When should you choose [Vintern-1B-v3_5](https://huggingface.co/5CD-AI/Vintern-1B-v3_5) vs Vintern-3B-R-beta? 🤔
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library_name: transformers
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license: mit
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datasets:
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- 5CD-AI/LLaVA-CoT-o1-Instruct
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language:
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- vi
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- en
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pipeline_tag: image-text-to-text
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---
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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!
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- 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.
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- Despite the difficulty of balancing multiple tasks alongside reasoning, Vintern-3B-R-beta has outperformed all previous versions across various benchmarks!
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When should you choose [Vintern-1B-v3_5](https://huggingface.co/5CD-AI/Vintern-1B-v3_5) vs Vintern-3B-R-beta? 🤔
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