Multi-View Relational Distillation for Spatial Reasoning with Vision-Language Models
Paper โข 2608.10864 โข Published
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")How to use kietngt00/MVRD with vLLM:
# 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
}'docker model run hf.co/kietngt00/MVRD
How to use kietngt00/MVRD with SGLang:
# 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
}'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
}'How to use kietngt00/MVRD with Docker Model Runner:
docker model run hf.co/kietngt00/MVRD
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.