docs: add README describing checkpoint layout and training code mapping

README.md

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+---
+license: apache-2.0
+library_name: transformers
+tags:
+  - vision-encoder
+  - distillation
+  - video-language
+  - siglip2
+  - dinov3
+---
+
+# VisionEncoder Checkpoints
+
+Final model checkpoints from the **VisionEncoder** research project.
+
+**Training code**: https://github.com/xiaomoguhz/VisionEncoder
+
+## Contents
+
+Each directory corresponds to one training pipeline in the code repo:
+
+| Directory | Training code |
+|---|---|
+| `declip_siglip2/spatial_align/` | `declip_siglip2/` — DeCLIP spatial alignment distillation on SigLIP2 using DINOv2 / DINOv3 as teacher |
+| `kd_mllm/s1_kd_pretrain/` | `ms-swift/kd_mllm/` stage-1 pretrain (`ms-swift/run_s1.sh`) |
+| `kd_mllm/s1_siglip2_qwen3_4b/` | `ms-swift/kd_mllm/` stage-1, SigLIP2 + Qwen3-4B backbone |
+| `kd_mllm/s2_siglip2_qwen3_4b_10pct/` | `ms-swift/kd_mllm/` stage-2 SFT on 10% data (`run_s2.sh`) |
+| `self_refine/qwen3vl_2b_10pct/` | `ms-swift/self_refine/` — register token injection + auto-calibrated GP threshold loss |
+| `video_mllm_swift/s1_siglip2_qwen3_1.7b/` | `ms-swift/video_mllm/` stage-1 with SigLIP2 encoder |
+| `video_mllm_swift/s1_declip_siglip2_qwen3_1.7b/` | `ms-swift/video_mllm/` stage-1 with DeCLIP-SigLIP2 encoder |
+| `video_mllm_swift/s2_siglip2_qwen3_1.7b_10pct/` | `ms-swift/video_mllm/` stage-2 SFT, SigLIP2 |
+| `video_mllm_swift/s2_declip_siglip2_qwen3_1.7b_10pct/` | `ms-swift/video_mllm/` stage-2 SFT, DeCLIP-SigLIP2 |
+| `video_mllm_swift/s2_image_only_10pct/` | Ablation: image-only stage-2 training |
+| `ms-swift-data/` | Not a checkpoint — preprocessed SFT training data (`ms-swift/data/`) used by the pipelines above |
+
+## Related repositories
+
+- **Code**: https://github.com/xiaomoguhz/VisionEncoder
+- **Evaluation data (~323 GB tarballs)**: https://huggingface.co/datasets/xiaomoguhzz/R3-Bench-data