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README.md

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+---
+license: apache-2.0
+library_name: transformers
+pipeline_tag: image-text-to-text
+base_model: OpenGVLab/InternVL3_5-8B
+tags:
+- multimodal
+- vision-language
+- spatial-reasoning
+- spatiolm
+---
+
+# SpatioLM-Perception-InternVL3.5
+
+This is the official **SpatioLM Perception** checkpoint based on
+**InternVL3.5 (8B)**. It is intended for metric depth and physical spatial perception.
+
+SpatioLM adds a plug-and-play spatio-vision module to a frozen vision-language
+model and learns physically coherent representations from pseudo depth and
+camera-ray supervision. No additional 3D input is required at inference time.
+
+## Installation
+
+```bash
+git clone https://github.com/xiaomi-research/spatio-lm.git
+cd spatio-lm
+pip install -e .
+```
+
+## Image inference
+
+```python
+import torch
+from lmms_eval.models.simple.internvl2 import load_image
+from PIL import Image
+from transformers import AutoTokenizer
+
+from spatiolm.models import InternVL3RChatModel
+
+checkpoint = "xiaomi-research/SpatioLM-Perception-InternVL3.5"
+image = Image.open("/path/to/image.jpg").convert("RGB")
+
+model = InternVL3RChatModel.from_pretrained(
+    checkpoint,
+    dtype=torch.bfloat16,
+    low_cpu_mem_usage=True,
+).eval().cuda()
+tokenizer = AutoTokenizer.from_pretrained(
+    checkpoint,
+    trust_remote_code=True,
+    use_fast=False,
+)
+pixel_values = load_image(image, input_size=448).to(
+    device="cuda",
+    dtype=torch.bfloat16,
+)
+answer = model.chat(
+    tokenizer,
+    pixel_values,
+    "Which object is closer to the camera?",
+    {"max_new_tokens": 128, "do_sample": False},
+)
+print(answer)
+```
+
+For video inference, benchmark evaluation, training details, and the Action
+checkpoint interface, see the
+[SpatioLM repository](https://github.com/xiaomi-research/spatio-lm).
+
+## Intended use and limitations
+
+- This checkpoint is intended for research on physical spatial intelligence.
+- Outputs can be inaccurate and should not be used as the sole signal in
+  safety-critical or high-impact decisions.
+- Performance can vary with image quality, viewpoint, scene domain, prompting,
+  and video sampling strategy.
+- The custom SpatioLM model implementation is required; loading with only stock
+  Transformers auto classes is not supported.
+
+## Citation
+
+```bibtex
+@inproceedings{wu2026spatiolm,
+  title={SpatioLM: Towards General Physical Spatial Intelligence in Vision-Language Models},
+  author={Wu, Jianhua and others},
+  booktitle={International Conference on Machine Learning},
+  year={2026}
+}
+```