Add pipeline tag, paper link, and sample usage

Hi, I'm Niels, part of the community science team at Hugging Face. This PR aims to improve the model card for HDTree by:
- Adding the `unconditional-image-generation` pipeline tag to the metadata for better discoverability.
- Adding a link to the research paper: [HDTree: Generative Modeling of Cellular Hierarchies for Robust Lineage Inference](https://huggingface.co/papers/2506.23287).
- Including sample usage instructions for validating the checkpoints based on the official GitHub repository.
- Linking the project page and source code for better accessibility.

README.md

@@ -1,6 +1,7 @@
 ---
-license: mit
 library_name: pytorch
+license: mit
+pipeline_tag: unconditional-image-generation
 tags:
 - hdtree
 - pytorch
@@ -11,9 +12,12 @@ tags:
 
 # HDTree ICML Checkpoints
 
-This repository hosts pretrained checkpoints for the public HDTree ICML release.
+This repository hosts pretrained checkpoints for the model presented in the paper [HDTree: Generative Modeling of Cellular Hierarchies for Robust Lineage Inference](https://huggingface.co/papers/2506.23287).
 
-Code: https://github.com/zangzelin/code_HDTree_icml
+HDTree is a generative modeling framework designed for robust lineage inference. It captures tree relationships within a hierarchical latent space using a unified hierarchical codebook and employs a quantized diffusion process to model continuous cell state transitions.
+
+- **Code:** [https://github.com/zangzelin/code_HDTree_icml](https://github.com/zangzelin/code_HDTree_icml)
+- **Project Page:** [https://zangzelin.github.io/code_HDTree_icml/](https://zangzelin.github.io/code_HDTree_icml/)
 
 ## Files
 
@@ -22,6 +26,25 @@ Code: https://github.com/zangzelin/code_HDTree_icml
 | `checkpoints/mnist/hdtree_mnist_best_epoch59_acc0.97570.pth` | MNIST | `configs/mnist.yaml` | Best MNIST checkpoint from the full run by checkpoint validation accuracy. |
 | `checkpoints/limb/hdtree_limb_i10_epoch199_acc0.53921.pth` | Limb | `configs/limb.yaml` default | Limb sweep i10/default checkpoint. |
 
+## Sample Usage
+
+To validate a trained checkpoint using the official code, you can use the provided validation script:
+
+```bash
+# Example for MNIST
+bash scripts/validate_checkpoint.sh mnist checkpoints/mnist/hdtree_mnist_best_epoch59_acc0.97570.pth
+```
+
+To compute reconstruction and log-likelihood with diffusion sampling, enable generation using the following command:
+
+```bash
+python main.py validate \
+  -c configs/mnist.yaml \
+  --model.init_args.ckpt_path=checkpoints/mnist/hdtree_mnist_best_epoch59_acc0.97570.pth \
+  --model.init_args.training_str=step2_r \
+  --model.init_args.gen_data_bool=True
+```
+
 ## Reported Metrics
 
 MNIST full run summary:
@@ -42,15 +65,9 @@ The included `logs/` files contain the original run outputs used to record these
 
 ```bash
 pip install huggingface_hub
-huggingface-cli download zangzelin/HDTree-ICML-checkpoints --local-dir .
-```
-
-Then use the public code repository's validation script, for example:
-
-```bash
-bash scripts/validate_checkpoint.sh checkpoints/limb/hdtree_limb_i10_epoch199_acc0.53921.pth
+huggingface-cli download zelinzang/HDTree-ICML-checkpoints --local-dir .
 ```
 
 ## Checksums
 
-See `SHA256SUMS`.
+See `SHA256SUMS`.