Add illustrated model card, family links and generation example

README.md

@@ -21,7 +21,7 @@ datasets:
 
 [Paper](https://arxiv.org/abs/2602.13215) · [Code](https://github.com/HaoranZhengRaul/AMOR) · [Files](https://huggingface.co/FlyinGodzilla/AMOR-GatedDeltaNet-180M/tree/main) · [All checkpoints](#model-family)
 
-The larger 1.5B checkpoint is being uploaded; its link will appear in the model-family table below.
+Looking for the larger checkpoint? [AMOR-Gated DeltaNet 1.5B](https://huggingface.co/FlyinGodzilla/AMOR-GatedDeltaNet-1.5B).
 
 ## The idea
 
@@ -41,8 +41,8 @@ position for future retrieval.
 their own pre-normalization. A shared, embedding-tied LM head supplies all three
 entropy evaluations and the final output prediction.*
 
-This checkpoint uses the **native entropy gate**. The optional distilled
-deployment routers studied in the paper are not included. Training uses dense
+This checkpoint uses the **native entropy gate**. Optional distilled
+deployment routers are not included in this release. Training uses dense
 attention with an output mask, so gating does not reduce training attention FLOPs.
 
 ## This checkpoint
@@ -142,7 +142,7 @@ stored once and restored by the loader. Optimizer state is excluded.
 | Backbone | 180M | 440M | 1.5B |
 |---|---|---|---|
 | Mamba2 | [180M](https://huggingface.co/FlyinGodzilla/AMOR-Mamba2-180M) | [440M](https://huggingface.co/FlyinGodzilla/AMOR-Mamba2-440M) | [1.5B](https://huggingface.co/FlyinGodzilla/AMOR-Mamba2-1.5B) |
-| Gated DeltaNet | [180M](https://huggingface.co/FlyinGodzilla/AMOR-GatedDeltaNet-180M) | [440M](https://huggingface.co/FlyinGodzilla/AMOR-GatedDeltaNet-440M) | 1.5B (upload in progress) |
+| Gated DeltaNet | [180M](https://huggingface.co/FlyinGodzilla/AMOR-GatedDeltaNet-180M) | [440M](https://huggingface.co/FlyinGodzilla/AMOR-GatedDeltaNet-440M) | [1.5B](https://huggingface.co/FlyinGodzilla/AMOR-GatedDeltaNet-1.5B) |
 
 All six checkpoints use the three-block canonical AMOR layout. Choose a backbone
 and scale to match your experiment; larger models require more memory.
@@ -158,7 +158,7 @@ storage still grows with context. Training context is 3,072 tokens; long-context
 experiments do not establish reliable performance on every domain.
 
 ## License
-The code and model weights are released under the MIT license; see [LICENSE](LICENSE).
+The code and model weights are released under the MIT license; see [LICENSE](https://huggingface.co/FlyinGodzilla/AMOR-GatedDeltaNet-180M/blob/main/LICENSE).
 
 ## Citation
 ```bibtex

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