Add model card (PLUME-7B: latent-reasoning universal multimodal embedding)

README.md

@@ -1,3 +1,66 @@
 ---
 license: apache-2.0
+base_model:
+- zhibinlan/UME-R1-7B
+language:
+- en
+tags:
+- multimodal-embedding
+- universal-multimodal-embedding
+- retrieval
+- latent-reasoning
+- mllm
+- qwen2-vl
+pipeline_tag: feature-extraction
+library_name: transformers
 ---
+
+# PLUME-7B
+
+**PLUME** (Latent Reasoning Based Universal Multimodal Embedding) is a 7B universal multimodal embedding model that maps heterogeneous inputs — text, images, videos, and visual documents — into a single shared retrieval space.
+
+Recent universal multimodal embedding (UME) methods improve retrieval by generating explicit chain-of-thought (CoT) rationales before extracting an embedding. This is effective but slow, and it forces rich multimodal evidence through a narrow textual bottleneck. PLUME instead replaces verbalized CoT with a **short autoregressive rollout of continuous latent states**, and uses a **semantic-anchor-guided transition adapter** to steer the latent computation along input-dependent reasoning trajectories under a fixed compute budget. The model is trained with a **progressive explicit-to-latent curriculum** that uses verbalized reasoning as a temporary training scaffold and gradually transfers it into hidden-state computation, eliminating explicit CoT at inference.
+
+This checkpoint is built on the **UME-R1-7B** backbone (Qwen2-VL-7B architecture).
+
+## Highlights
+
+- **Universal**: a single model for text / image / video / visual-document embeddings.
+- **Latent reasoning**: fewer than 10 latent steps replace hundreds of generated CoT tokens, giving **>30× faster inference** than explicit-CoT UME at comparable or better quality.
+- **Strong retrieval**: evaluated on the 78-task **MMEB-v2** benchmark, outperforming strong explicit-CoT UME baselines — especially where evidence is dense and structurally complex (video and visual-document retrieval).
+
+## Model details
+
+- **Backbone**: [`zhibinlan/UME-R1-7B`](https://huggingface.co/zhibinlan/UME-R1-7B) (Qwen2-VL-7B, `Qwen2VLForConditionalGeneration`)
+- **Parameters**: ~7B, weights in half precision (4 safetensors shards, ~17 GB)
+- **License**: Apache-2.0
+
+## Usage
+
+The weights load as a standard Qwen2-VL checkpoint:
+
+```python
+from transformers import AutoProcessor, Qwen2VLForConditionalGeneration
+
+model = Qwen2VLForConditionalGeneration.from_pretrained(
+    "Rem520/PLUME-7B", torch_dtype="auto", device_map="auto"
+)
+processor = AutoProcessor.from_pretrained("Rem520/PLUME-7B")
+```
+
+To use the full PLUME embedding pipeline (latent rollout + semantic-anchor-guided transition adapter), follow the official code: **https://github.com/haoxiangzhao12138/PLUME**
+
+## Citation
+
+```bibtex
+@article{he2026plume,
+  title   = {PLUME: Latent Reasoning Based Universal Multimodal Embedding},
+  author  = {He, Chenwei and Hao, Xiangzhao and Yang, Tianyu and Ma, Yuxiang and
+             Jia, Yuheng and Wu, Lingxiang and Zhao, Chaoyang and Guo, Haiyun and Wang, Jinqiao},
+  journal = {arXiv preprint arXiv:2604.02073},
+  year    = {2026}
+}
+```
+
+- **Paper**: [arXiv:2604.02073](https://arxiv.org/abs/2604.02073)
+- **Code**: [github.com/haoxiangzhao12138/PLUME](https://github.com/haoxiangzhao12138/PLUME)