Add model card: attribution, licences, training config

README.md

@@ -1,3 +1,91 @@
 ---
-license: apache-2.0
+license: cc-by-4.0
+base_model: Wan-AI/Wan2.1-T2V-1.3B
+pipeline_tag: text-to-video
+tags:
+  - world-model
+  - video-generation
+  - action-conditioned
+  - memory
+  - ablation
+  - research-artifact
 ---
+
+# echo — Echo-Memory ablation arm (`ucpe` + two-chunk memory)
+
+> **This is an unofficial derivative artifact, not the official Echo-Memory release.**
+> It contains one ablation arm re-trained from the Echo-Memory codebase, produced for
+> internal study. For the official code, paper and checkpoints, use the links below.
+
+## Upstream / attribution
+
+| | |
+|---|---|
+| Original work | **Echo-Memory: A Controlled Study of Memory in Action World Models** |
+| Authors | Echo Team @ Joy Future Academy, JD |
+| Paper | [arXiv:2606.09803](https://arxiv.org/abs/2606.09803) |
+| Official code | https://github.com/Echo-Team-Joy-Future-Academy-JD/Echo-Memory |
+| Official checkpoints | https://huggingface.co/Echo-Team/Echo-Memory |
+| Code license | CC BY 4.0 |
+| Base model | [Wan2.1-T2V-1.3B](https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B) (Apache 2.0) |
+
+The Echo-Memory code included here is redistributed under CC BY 4.0 with attribution to
+the Echo Team. The weights are derived from Wan2.1-T2V-1.3B, which is Apache 2.0; that
+license and its notices continue to apply to the derived weights.
+
+## What this repository contains
+
+```
+code/                       Echo-Memory codebase as used for this run
+checkpoints/
+  Step-10000.safetensors    3.5 GB
+  Step-20000.safetensors    3.5 GB
+  Step-30000.safetensors    3.5 GB   (final, max_train_steps = 30000)
+```
+
+## Training configuration
+
+Single arm of a memory-mechanism ablation, run name
+`memory_baselines_basic_abl_ucpe_memory_two_chunk`.
+
+- Base weights: `Wan2.1-T2V-1.3B` (DiT + `umt5-xxl` text encoder + `Wan2.1_VAE`)
+- Trainable: `dit` only, `--remove_prefix_in_ckpt pipe.dit.`
+- Memory: `--enable_context_memory --context_source replay`
+  `--prev_chunk_frames 81 --context_memory_frames 5`
+- Mechanism under test: `--use_cgla_memory --cgla_mechanism ucpe`
+  `--cgla_every_n_blocks 4 --cgla_aux_loss_weight 0.01`
+- Also enabled: `--use_moc --moc_temperature 1.0`, `--train_cam_pose`,
+  `--add_action_attn`, `--use_rt_relative`, `--use_anchor_frame`, `--cfg_target_only`
+- Resolution / length: 352 x 640, 81 frames
+- Optimisation: lr 2e-5, 1 epoch, batch 1/device, grad-accum 1,
+  `--timestep_shift 15`, `--context_drop_prob 0.1`, 30,000 steps
+- Hardware: 1x NVIDIA H20
+
+## Data — not redistributed here
+
+Training used the **Context-as-Memory** dataset (Unreal-Engine rendered environments,
+~331 GB of frames). It is **not** included in this repository: it is third-party data
+and the copy used here carried no redistribution licence. Obtain it from its original
+source.
+
+## Status and caveats
+
+- This is **one arm of an ablation sweep**, uploaded as a research artifact.
+  Its results had not been analysed at the time of upload — no quality claim is made,
+  and it should not be read as the best or recommended configuration.
+- It is **not** a reproduction of, or a replacement for, the official Echo-Memory
+  checkpoints. Comparisons against the paper's numbers are not valid without matching
+  the official evaluation protocol.
+
+## Citation
+
+Please cite the original work:
+
+```bibtex
+@article{echomemory2026,
+  title  = {Echo-Memory: A Controlled Study of Memory in Action World Models},
+  author = {Echo Team, Joy Future Academy, JD},
+  journal= {arXiv preprint arXiv:2606.09803},
+  year   = {2026}
+}
+```