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README.md

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+---
+language:
+- en
+tags:
+- causal-lm
+library_name: transformers
+license: apache-2.0
+datasets:
+- allenai/dolma
+---
+
+# Gemstone-512x16_lr_ablation
+Gemstone-512x16_lr_ablation is part of the [Gemstone Suite of Models](https://huggingface.co/collections/tomg-group-umd/gemstone-models-679408ee3f19f1d4d00e8b10). A set of models trained with varying widths and depths. This particular version, denoted by the `_lr_ablation` postfix, corresponds to an ablation detailed in the paper where we train the same suite of models but with a learning rate that is half of the original.
+
+## Training
+We train using [litgpt](https://github.com/Lightning-AI/litgpt) and [AxoNN](https://github.com/axonn-ai/litgpt) using AMD MI250X GPUs on [Frontier](https://www.olcf.ornl.gov/olcf-resources/compute-systems/frontier/) at Oak Ridge National Laboratory with a global batch size of 2048.
+
+## Data
+Train and validation data is taken from non-overlapping subsets of [dolma](https://huggingface.co/datasets/allenai/dolma). As such it is _not_ an instruction model.
+This model is trained for 100 billion tokens (in contrast to the main suite, which is trained to 350 billion tokens), we upload checkpoints every 2 billion tokens (477 steps).
+
+## Using Gemstone-512x16_lr_ablation
+The Gemstones are based on the [gemma-2b](https://huggingface.co/google/gemma-2b) architecture and use [modeling_gemma.py](https://github.com/huggingface/transformers/blob/main/src/transformers/models/gemma/modeling_gemma.py) to run using the transformers library.
+
+## Licence
+This model is released under the [apache-2.0](https://choosealicense.com/licenses/apache-2.0/) licence.
+
+## Contact
+Please, feel free to contact us with any questions, or open a discussion thread.
+
+# Citation
+```
+@article{mcleish2024gemstones
+    title={Gemstones: A Model Suite for Multi-Faceted Scaling Laws}, 
+    author={Sean McLeish and John Kirchenbauer and David Yu Miller and Siddharth Singh and Abhinav Bhatele and Micah Goldblum and Ashwinee Panda and Tom Goldstein},
+    journal={arXiv preprint arXiv:2502.},
+    year={2025}
+}
+```