Image-Text-to-Text
Transformers
ONNX
Safetensors
English
vision-language-action
edge-deployment
tensorRT
qwen
Instructions to use xintaozhen/MiniVLA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xintaozhen/MiniVLA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="xintaozhen/MiniVLA")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("xintaozhen/MiniVLA", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use xintaozhen/MiniVLA with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "xintaozhen/MiniVLA" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "xintaozhen/MiniVLA", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/xintaozhen/MiniVLA
- SGLang
How to use xintaozhen/MiniVLA 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 "xintaozhen/MiniVLA" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "xintaozhen/MiniVLA", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "xintaozhen/MiniVLA" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "xintaozhen/MiniVLA", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use xintaozhen/MiniVLA with Docker Model Runner:
docker model run hf.co/xintaozhen/MiniVLA
Update README.md
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README.md
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library_name: transformers
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datasets:
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- LIBERO
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pipeline_tag: text-to-
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# MiniVLA
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- Exported the **vision encoder** to TensorRT, reducing perception latency and GPU memory usage.
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- Integrated **Qwen 2.5 0.5B** in Hugging Face and TensorRT-LLM formats.
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- Designed a **modular system architecture** with router & fallback for robustness.
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- Demonstrated efficient **edge-side VLA inference**
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---
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library_name: transformers
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datasets:
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- LIBERO
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pipeline_tag: image-text-to-text
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---
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# MiniVLA
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- Exported the **vision encoder** to TensorRT, reducing perception latency and GPU memory usage.
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- Integrated **Qwen 2.5 0.5B** in Hugging Face and TensorRT-LLM formats.
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- Designed a **modular system architecture** with router & fallback for robustness.
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- Demonstrated efficient **edge-side VLA inference**
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on Jetson Orin Nano in LIBERO tasks.
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