Instructions to use kietngt00/MVRD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kietngt00/MVRD with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="kietngt00/MVRD")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("kietngt00/MVRD", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use kietngt00/MVRD with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "kietngt00/MVRD" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "kietngt00/MVRD", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/kietngt00/MVRD
- SGLang
How to use kietngt00/MVRD 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 "kietngt00/MVRD" \ --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": "kietngt00/MVRD", "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 "kietngt00/MVRD" \ --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": "kietngt00/MVRD", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use kietngt00/MVRD with Docker Model Runner:
docker model run hf.co/kietngt00/MVRD
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Download README.md from kietngt00/MVRD: direct link, hf CLI and curl.
- Browser
- Download file 667 Bytes
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https://huggingface.co/kietngt00/MVRD/resolve/main/README.md
- Command line
-
hf download hf://kietngt00/MVRD/README.md
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curl -L -o README.md https://huggingface.co/kietngt00/MVRD/resolve/main/README.md
667 Bytes
metadata
pipeline_tag: image-text-to-text
library_name: transformers
MVRD: Multi-View Relational Distillation for Spatial Reasoning
This repository contains the model presented in the paper Multi-View Relational Distillation for Spatial Reasoning with Vision-Language Models. It is built on LLaVA-Video-7B-Qwen2 and includes additional grounding tokens for spatial reasoning tasks such as visual spatial understanding and 3D scene understanding.
For training and evaluation instructions, please refer to the GitHub repository.