Visual Question Answering
Transformers
Safetensors
English
bunny-phi
text-generation
Embodied AI
MLLM
VLM
Spatial Understanding
Phi-2
Instructions to use RussRobin/SpatialBot-3B-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RussRobin/SpatialBot-3B-LoRA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="RussRobin/SpatialBot-3B-LoRA")# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("RussRobin/SpatialBot-3B-LoRA", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: cc-by-4.0 | |
| datasets: | |
| - RussRobin/SpatialQA | |
| language: | |
| - en | |
| tags: | |
| - Embodied AI | |
| - MLLM | |
| - VLM | |
| - Spatial Understanding | |
| - Phi-2 | |
| pipeline_tag: visual-question-answering | |
| SpatialBot is a VLM with spatial understanding and reasoning abilties, by precisely understanding depth maps and using them to do high-level tasks. | |
| In this HF repo, we provide ckpts of SpatialBot-3B with LoRA, which is based on Phi-2 and SigLIP. It can perform well on general VLM tasks and spatial understanding benchmarks like SpatialBench. | |
| You will also need to download [pretrained CKPT](https://huggingface.co/RussRobin/SpatialBot-3B-pretrain). | |
| ### Paper: | |
| https://arxiv.org/abs/2406.13642 | |
| ### GitHub repo: | |
| https://github.com/BAAI-DCAI/SpatialBot | |
| <!-- ### SpatialQA, the training set: | |
| https://huggingface.co/datasets/RussRobin/SpatialQA --> | |
| ### SpatialBench, the benchmark: | |
| https://huggingface.co/datasets/RussRobin/SpatialBench | |
| ### Merged SpatialBot-3B: | |
| https://huggingface.co/RussRobin/SpatialBot-3B |