Card: header and introduction of ShellElastic

README.md

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 license: cc-by-4.0
 language:
 - en
-pretty_name: ShellElastic pretraining data
+pretty_name: ShellElastic data
 size_categories:
 - 10M<n<100M
 task_categories:
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-# ShellElastic pretraining data (Table 2)
+<div align="center">
 
-The tokenized training and validation data of the Table 2 runs of *ShellElastic: Scalable Elastic Pretraining with Simultaneous Maximal Updates and Hyperparameter Transfer* (Jin et al.; Rutgers University and Adobe Research). As described in Section 4.1 of the paper,
+# ShellElastic
+
+### Scalable Elastic Pretraining with Simultaneous Maximal Updates and Hyperparameter Transfer
+
+**Can Jin**<sup>1*</sup> · **Yan Kang**<sup>2</sup> · **Yifan Gong**<sup>2</sup> · **Chongjian Ge**<sup>2</sup> · **Mingze Xu**<sup>2</sup> · **Yuanjun Xiong**<sup>2</sup> · **Dimitris N. Metaxas**<sup>1</sup> · **Hongwu Peng**<sup>2</sup>
+
+<sup>1</sup>Rutgers University &emsp; <sup>2</sup>Adobe Research
+
+<sup>*</sup>Corresponding author: [can.jin@rutgers.edu](mailto:can.jin@rutgers.edu). Work done during an internship at Adobe Research.
+
+<a href="https://github.com/jincan333/ShellElastic/tree/main/paper"><img alt="Paper" src="https://img.shields.io/badge/Paper-PDF-b31b1b?style=for-the-badge"></a>
+<a href="http://localhost:8765/"><img alt="Project Page" src="https://img.shields.io/badge/Project-Page-2f6db5?style=for-the-badge"></a>
+<a href="https://github.com/jincan333/ShellElastic"><img alt="Code" src="https://img.shields.io/badge/Code-GitHub-181717?style=for-the-badge&logo=github&logoColor=white"></a>
+<a href="https://huggingface.co/collections/Can111/shellelastic-checkpoints-and-data-6ac41e3a763d5a3fd0e79111"><img alt="Checkpoints" src="https://img.shields.io/badge/Checkpoints-Hugging%20Face-ffd21e?style=for-the-badge&logo=huggingface&logoColor=white"></a>
+<a href="https://huggingface.co/collections/Can111/shellelastic-checkpoints-and-data-6ac41e3a763d5a3fd0e79111"><img alt="Datasets" src="https://img.shields.io/badge/Datasets-Hugging%20Face-ffd21e?style=for-the-badge&logo=huggingface&logoColor=white"></a>
+<a href="https://github.com/jincan333/ShellElastic/blob/main/LICENSE"><img alt="License" src="https://img.shields.io/badge/License-Apache--2.0-3da639?style=for-the-badge&logo=apache&logoColor=white"></a>
+
+</div>
+
+## About ShellElastic
+
+Foundation models need to support diverse inference budgets. Elastic models train several nested sub-networks inside one weight-shared supernet. ShellElastic is an elastic transformer that organizes hidden dimensions into a dense core and successive shells, so that every hidden matrix is block lower triangular. One full-width forward pass contains the exact hidden states of every sub-network, so joint training keeps a fixed computation graph and costs about as much per step as training the widest sub-network alone.
+
+µ-Elastic extends maximal update parameterization (µP) to ShellElastic and other elastic architectures. Its scaling rules keep all sub-networks of a supernet maximally updated simultaneously, and hyperparameters tuned on a small non-elastic proxy stay near-optimal for elastic families and larger supernets. A single ShellElastic run produces sub-networks that outperform MatFormer and fully elastic baselines at matched parameter counts, and approach separately trained models that together take several times the steps.
+
+## Tokenized training and validation data
+
+The tokenized training and validation data of the runs of the paper. As described in Section 4.1 of the paper,
 training uses [DCLM-Baseline](https://huggingface.co/datasets/mlfoundations/dclm-baseline-1.0) (Li et al., 2024) and validation held-out [C4](https://huggingface.co/datasets/allenai/c4) text (Raffel et al.,
 2020), tokenized with the [GPT-NeoX-20B tokenizer](https://huggingface.co/EleutherAI/gpt-neox-20b) (Black et al., 2022) and packed into sequences of 2,049
 tokens. This repository holds exactly the shards that the runs read, in the layout that the code of the paper reads.
-The final checkpoints of these runs are in the model repositories [Can111/ShellElastic-119M-285M](https://huggingface.co/Can111/ShellElastic-119M-285M), [Can111/ShellElastic-375M-906M](https://huggingface.co/Can111/ShellElastic-375M-906M) and [Can111/ShellElastic-675M-1.69B](https://huggingface.co/Can111/ShellElastic-675M-1.69B) and the baseline repositories
-`Can111/ShellElastic-baseline-*`.
+The final checkpoints of these runs are in the [ShellElastic collection](https://huggingface.co/collections/Can111/shellelastic-checkpoints-and-data-6ac41e3a763d5a3fd0e79111).
 
 ## Contents