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.
docker model run hf.co/kietngt00/MVRD