Create README.md

README.md

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+---
+license: apache-2.0
+base_model:
+- google/siglip-so400m-patch14-384
+pipeline_tag: image-feature-extraction
+---
+# Model Card for CoMP-MM-1B
+
+<!-- Provide a quick summary of what the model is/does. -->
+This is an VFM that supports <b>native image resolution inputs</b>, continually pre-trained from [SigLIP](https://huggingface.co/google/siglip-so400m-patch14-384).
+
+## Model Sources
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [https://github.com/SliMM-X/CoMP-MM]
+- **Paper:** [https://arxiv.org/abs/2503.18931]
+
+
+## How to Get Started with the Model
+
+Install the github repo, and use the code below to get started with the model.
+
+```python
+import torch
+from slimm.model.processor import SliMMQwen2VLProcessor
+from slimm.model.utils_vl import process_vision_info
+from slimm.model.vision_encoder import CoMPSiglipVisionModel
+from PIL import Image
+
+model_path = "SliMM-X/CoMP-SigLIP-So400M"
+
+model = CoMPSiglipVisionModel.from_pretrained(
+    model_path, torch_dtype="auto", device_map="cuda", w_merger=False
+).to(torch.bfloat16)
+
+
+processor = SliMMQwen2VLProcessor.from_pretrained(model_path)
+
+image_input = Image.open("https://slimm-x.github.io/comp/figs/teaser.png")
+inputs = processor(
+    images=image_input,
+    return_tensors="pt",
+)
+
+inputs = inputs.to("cuda")
+output_feat = model(inputs.pixel_values.to(torch.bfloat16), inputs.image_grid_thw)
+print(output_feat)
+```
+
+## Citation
+
+
+**BibTeX:**
+
+```bibtex
+@article{comp2025,
+      title={CoMP: Continual Multimodal Pre-training for Vision Foundation Models}, 
+      author={Chen, Yitong and Meng, Lingchen and Peng, Wujian and Wu, Zuxuan and Jiang, Yu-Gang},
+      year={2025},
+      journal={arXiv preprint arXiv:2503.18931}, 
+}
+```